The field of Artificial Intelligence (AI) has always faced two major challenges. The first is that data is kept scattered and cannot be collected for more efficiently use. The second is that data privacy and security need to be continuously strengthened. Based on these two points, federated learning is proposed as an emerging machine learning scheme. The idea of federated learning is to collaboratively train neural networks on servers. Each user receives the current weights of the network and then sequentially sends parameter updates (gradients) based on their own data. Because the input data remains on-device and only the parameter gradients are shared, this scheme is considered to be effective in preserving data privacy. Some previous attacks also provide a false sense of security since they only succeed in contrived settings, even for a single image. Our research mainly focus on attacks on shared gradients, showing experimentally that private training data can be obtained from publicly shared gradients. We do experiments on both linear-based and convolutional-based deep networks, whose results show that our attack is capable of creating a threat to data privacy, and this threat is independent of the specific structure of neural networks. The method presented in this paper is only to illustrate that it is feasible to recover user data from shared gradients, and cannot be used as an attack to obtain privacy in large quantities. The goal is to spark further research on federated learning, especially gradient security. We also make some brief discussion on possible strategies against our attack methods of privacy. Different methods have their own advantages and disadvantages in terms of privacy protection. Therefore, data pre-processing and network structure adjustment may need to be further researched, so that the process of training the models can achieve better privacy protection while maintaining high precision. / Området artificiell intelligens har alltid stått inför två stora utmaningar. Den första är att data hålls utspridda och inte kan samlas in för mer effektiv användning. Det andra är att datasekretess och säkerhet behöver stärkas kontinuerligt. Baserat på dessa två punkter föreslås federerat lärande som ett framväxande angreppssätt inom maskininlärning. Tanken med federerat lärande är att tillsammans träna neurala nätverk på servrar. Varje användare får nätverkets aktuella vikter och skickar sedan parameteruppdateringar (gradienter) sekventiellt baserat på sina egna data. Eftersom indata förblir på enheten och endast parametergradienterna delas, anses detta schema vara effektivt för att bevara datasekretessen. Vissa tidigare attacker ger också en falsk känsla av säkerhet eftersom de bara lyckas i konstruerade inställningar, även för en enda bild. Vår forskning fokuserar främst på attacker på delade gradienter, och visar experimentellt att privat träningsdata kan erhållas från offentligt delade gradienter. Vi gör experiment på både linjärbaserade och faltningsbaserade djupa nätverk, vars resultat visar att vår attack kan skapa ett hot mot dataintegriteten, och detta hot är oberoende av den specifika strukturen hos djupa nätverk. Metoden som presenteras i denna rapport är endast för att illustrera att det är möjligt att rekonstruera användardata från delade gradienter, och kan inte användas som en attack för att erhålla integritet i stora mängder. Målet är att få igång ytterligare forskning om federerat lärande, särskilt gradientsäkerhet. Vi gör också en kort diskussion om möjliga strategier mot våra attackmetoder för integritet. Olika metoder har sina egna fördelar och nackdelar när det gäller integritetsskydd. Därför kan förbearbetning av data och justering av nätverksstruktur behöva undersökas ytterligare, så att processen med att träna modellerna kan uppnå bättre integritetsskydd samtidigt som hög precision bibehålls.

<p dir="ltr">The parallel growth of contemporary machine learning (ML) technologies alongside edge/-fog networking has necessitated the development of novel paradigms to effectively manage their intersection. Specifically, the proliferation of edge devices equipped with data generation and ML model training capabilities has given rise to an alternative paradigm called federated learning (FL), moving away from traditional centralized ML common in cloud-based networks. FL involves training ML models directly on edge devices where data are generated.</p><p dir="ltr">A fundamental challenge of FL lies in the extensive heterogeneity inherent to edge/fog networks, which manifests in various forms such as (i) statistical heterogeneity: edge devices have distinct underlying data distributions, (ii) structural heterogeneity: edge devices have diverse physical hardware, (iii) data quality heterogeneity: edge devices have varying ratios of labeled and unlabeled data, and (iv) adversarial compromise: some edge devices may be compromised by adversarial attacks. This dissertation endeavors to capture and model these intricate relationships at the intersection of FL and highly heterogeneous edge/fog networks. To do so, this dissertation will initially develop closed-form expressions for the trade-offs between ML performance and resource cost considerations within edge/fog networks. Subsequently, it optimizes the fundamental processes of FL, encompassing aspects such as batch size control for stochastic gradient descent (SGD) and sampling for global aggregations. This optimization is jointly formulated with networking considerations, which include communication resource consumption and device-to-device (D2D) cooperation.</p><p dir="ltr">In the former half of the dissertation, the emphasis is first on optimizing device sampling for global aggregations in FL, and then on developing a self-sufficient hierarchical meta-learning approach for FL. These methodologies maximize expected ML model performance while addressing common challenges associated with statistical and system heterogeneity. Novel techniques, such as management of D2D data offloading, adaptive CPU clock cycle control, integration of meta-learning, and much more, enable these methodologies. In particular, the proposed hierarchical meta-learning approach enables rapid integration of new devices in large-scale edge/fog networks.</p><p dir="ltr">The latter half of the dissertation directs its ocus towards emerging forms of heterogeneity in FL scenarios, namely (i) heterogeneity in quantity and quality of local labeled and unlabeled data at edge devices and (ii) heterogeneity in terms of adversarially comprised edge devices. To deal with heterogeneous labeled/unlabeled data across edge networks, this dissertation proposes a novel methodology that enables multi-source to multi-target federated domain adaptation. This proposed methodology views edge devices as sources – devices with mostly labeled data that perform ML model training, or targets - devices with mostly unlabeled data that rely on sources’ ML models, and subsequently optimizes the network relationships. In the final chapter, a novel methodology to improve FL robustness is developed in part by viewing adversarial attacks on FL as a form of heterogeneity.</p>

Federated Learning (FL), one variant of Machine Learning (ML) technology, has emerged as a prevalent method for multiple parties to collaboratively train ML models in a distributed manner with the help of a central server normally supplied by a Cloud Service Provider (CSP). Nevertheless, many existing vulnerabilities pose a threat to the advantages of FL and cause potential risks to data security and privacy, such as data leakage, misuse of the central server, or the threat of eavesdroppers illicitly seeking sensitive information. Promisingly advanced cryptography technologies such as Homomorphic Encryption (HE) and Confidential Computing (CC) can be utilized to enhance the security and privacy of FL. However, the development of a framework that seamlessly combines these technologies together to provide confidential FL while retaining efficiency remains an ongoing challenge. In this degree project, we develop a lightweight and user-friendly FL framework called Heflp, which integrates HE and CC to ensure data confidentiality and integrity throughout the entire FL lifecycle. Heflp supports four HE schemes to fit diverse user requirements, comprising three pre-existing schemes and one optimized scheme that we design, named Flashev2, which achieves the highest time and spatial efficiency across most scenarios. The time and memory overheads of all four HE schemes are also evaluated and a comparison between the pros and cons of each other is summarized. To validate the effectiveness, Heflp is tested on the MNIST dataset and the Threat Intelligence dataset provided by CanaryBit, and the results demonstrate that it successfully preserves data privacy without compromising model accuracy. / Federated Learning (FL), en variant av Maskininlärning (ML)-teknologi, har framträtt som en dominerande metod för flera parter att samarbeta om att distribuerat träna ML-modeller med hjälp av en central server som vanligtvis tillhandahålls av en molntjänstleverantör (CSP). Trots detta utgör många befintliga sårbarheter ett hot mot FL:s fördelar och medför potentiella risker för datasäkerhet och integritet, såsom läckage av data, missbruk av den centrala servern eller risken för avlyssnare som olagligt söker känslig information. Lovande avancerade kryptoteknologier som Homomorf Kryptering (HE) och Konfidentiell Beräkning (CC) kan användas för att förbättra säkerheten och integriteten för FL. Utvecklingen av en ramverk som sömlöst kombinerar dessa teknologier för att erbjuda konfidentiellt FL med bibehållen effektivitet är dock fortfarande en pågående utmaning. I detta examensarbete utvecklar vi en lättviktig och användarvänlig FL-ramverk som kallas Heflp, som integrerar HE och CC för att säkerställa datakonfidentialitet och integritet under hela FLlivscykeln. Heflp stöder fyra HE-scheman för att passa olika användarbehov, bestående av tre befintliga scheman och ett optimerat schema som vi designar, kallat Flashev2, som uppnår högsta tids- och rumeffektivitet i de flesta scenarier. Tids- och minneskostnaderna för alla fyra HE-scheman utvärderas också, och en jämförelse mellan fördelar och nackdelar sammanfattas. För att validera effektiviteten testas Heflp på MNIST-datasetet och Threat Intelligence-datasetet som tillhandahålls av CanaryBit, och resultaten visar att det framgångsrikt bevarar datasekretessen utan att äventyra modellens noggrannhet.

As the era of Industry 4.0 arises, the number of devices that are connectedto a network has increased. The devices continuously generate data that hasvarious information from power consumption to the configuration of thedevices. Since the data have the raw information about each local node inthe network, the manipulation of the information brings a potential to benefitthe network with different methods. However, due to the large amount ofnon-IID data generated in each node, manual operations to process the dataand tune the methods became challenging. To overcome the challenge, therehave been attempts to apply automated methods to build accurate machinelearning models by a subset of collected data or cluster network nodes byleveraging clustering algorithms and using machine learning models withineach cluster. However, the conventional clustering algorithms are imperfectin a distributed and dynamic network due to risk of data privacy, the nondynamicclusters, and the fixed number of clusters. These limitations ofthe clustering algorithms degrade the performance of the machine learningmodels because the clusters may become obsolete over time. Therefore, thisthesis proposes a three-phase clustering algorithm in dynamic environmentsby leveraging 1) GAN-based clustering, 2) cluster calibration, and 3) divisiveclustering in federated learning. GAN-based clustering preserves data becauseit eliminates the necessity of sharing raw data in a network to create clusters.Cluster calibration adds dynamics to fixed clusters by continuously updatingclusters and benefits methods that manage the network. Moreover, the divisiveclustering explores the different number of clusters by iteratively selectingand dividing a cluster into multiple clusters. As a result, we create clustersfor dynamic environments and improve the performance of machine learningmodels within each cluster. / ett nätverk ökat. Enheterna genererar kontinuerligt data som har varierandeinformation, från strömförbrukning till konfigurationen av enheterna. Eftersomdatan innehåller den råa informationen om varje lokal nod i nätverket germanipulation av informationen potential att gynna nätverket med olika metoder.På grund av den stora mängden data, och dess egenskap av att vara icke-o.l.f.,som genereras i varje nod blir manuella operationer för att bearbeta data ochjustera metoderna utmanande. För att hantera utmaningen finns försök med attanvända automatiserade metoder för att bygga precisa maskininlärningsmodellermed hjälp av en mindre mängd insamlad data eller att gruppera nodergenom att utnyttja klustringsalgoritmer och använda maskininlärningsmodellerinom varje kluster. De konventionella klustringsalgoritmerna är emellertidofullkomliga i ett distribuerat och dynamiskt nätverk på grund av risken fördataskydd, de icke-dynamiska klusterna och det fasta antalet kluster. Dessabegränsningar av klustringsalgoritmerna försämrar maskininlärningsmodellernasprestanda eftersom klustren kan bli föråldrade med tiden. Därför föreslårdenna avhandling en trefasklustringsalgoritm i dynamiska miljöer genom attutnyttja 1) GAN-baserad klustring, 2) klusterkalibrering och 3) klyvning avkluster i federerad inlärning. GAN-baserade klustring bevarar dataintegriteteneftersom det eliminerar behovet av att dela rådata i ett nätverk för att skapakluster. Klusterkalibrering lägger till dynamik i klustringen genom att kontinuerligtuppdatera kluster och fördelar metoder som hanterar nätverket. Dessutomdelar den klövlande klustringen olika antal kluster genom att iterativt välja ochdela ett kluster i flera kluster. Som ett resultat skapar vi kluster för dynamiskamiljöer och förbättrar prestandan hos maskininlärningsmodeller inom varjekluster.

With the increasing number of wireless devices and the phenomenal amount of data that is being generated by them, there is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. However, managing the large-scale multi-dimensional data to maintain the efficiency and scalability of the ML algorithms has obviously been a challenge. Tensors provide a useful framework to represent multi-dimensional data in an integrated manner by preserving relationships in data across different dimensions. This thesis studies two new applications of tensors to ML for wireless communications where the tensor structure of the concerned data is exploited in novel ways. The first contribution of this thesis is a tensor learning-based low-complexity precoder codebook design technique for a full-dimension multiple-input multiple-output (FD-MIMO) system with a uniform planar antenna (UPA) array at the transmitter (Tx) whose channel distribution is available through a dataset. Represented as a tensor, the FD-MIMO channel is further decomposed using a tensor decomposition technique to obtain an optimal precoder which is a function of Kronecker-Product (KP) of two low-dimensional precoders, each corresponding to the horizontal and vertical dimensions of the FD-MIMO channel. From the design perspective, we have made contributions in deriving a criterion for optimal product precoder codebooks using the obtained low-dimensional precoders. We show that this product codebook design problem is an unsupervised clustering problem on a Cartesian Product Grassmann Manifold (CPM), where the optimal cluster centroids form the desired codebook. We further simplify this clustering problem to a $K$-means algorithm on the low-dimensional factor Grassmann manifolds (GMs) of the CPM which correspond to the horizontal and vertical dimensions of the UPA, thus significantly reducing the complexity of precoder codebook construction when compared to the existing codebook learning techniques. The second contribution of this thesis is a tensor-based bandwidth-efficient gradient communication technique for federated learning (FL) with convolutional neural networks (CNNs). Concisely, FL is a decentralized ML approach that allows to jointly train an ML model at the server using the data generated by the distributed users coordinated by a server, by sharing only the local gradients with the server and not the raw data. Here, we focus on efficient compression and reconstruction of convolutional gradients at the users and the server, respectively. To reduce the gradient communication overhead, we compress the sparse gradients at the users to obtain their low-dimensional estimates using compressive sensing (CS)-based technique and transmit to the server for joint training of the CNN. We exploit a natural tensor structure offered by the convolutional gradients to demonstrate the correlation of a gradient element with its neighbors. We propose a novel prior for the convolutional gradients that captures the described spatial consistency along with its sparse nature in an appropriate way. We further propose a novel Bayesian reconstruction algorithm based on the Generalized Approximate Message Passing (GAMP) framework that exploits this prior information about the gradients. Through the numerical simulations, we demonstrate that the developed gradient reconstruction method improves the convergence of the CNN model. / Master of Science / The increase in the number of wireless and mobile devices have led to the generation of massive amounts of multi-modal data at the users in various real-world applications including wireless communications. This has led to an increasing interest in machine learning (ML)-based data-driven techniques for communication system design. The native setting of ML is {em centralized} where all the data is available on a single device. However, the distributed nature of the users and their data has also motivated the development of distributed ML techniques. Since the success of ML techniques is grounded in their data-based nature, there is a need to maintain the efficiency and scalability of the algorithms to manage the large-scale data. Tensors are multi-dimensional arrays that provide an integrated way of representing multi-modal data. Tensor algebra and tensor decompositions have enabled the extension of several classical ML techniques to tensors-based ML techniques in various application domains such as computer vision, data-mining, image processing, and wireless communications. Tensors-based ML techniques have shown to improve the performance of the ML models because of their ability to leverage the underlying structural information in the data. In this thesis, we present two new applications of tensors to ML for wireless applications and show how the tensor structure of the concerned data can be exploited and incorporated in different ways. The first contribution is a tensor learning-based precoder codebook design technique for full-dimension multiple-input multiple-output (FD-MIMO) systems where we develop a scheme for designing low-complexity product precoder codebooks by identifying and leveraging a tensor representation of the FD-MIMO channel. The second contribution is a tensor-based gradient communication scheme for a decentralized ML technique known as federated learning (FL) with convolutional neural networks (CNNs), where we design a novel bandwidth-efficient gradient compression-reconstruction algorithm that leverages a tensor structure of the convolutional gradients. The numerical simulations in both applications demonstrate that exploiting the underlying tensor structure in the data provides significant gains in their respective performance criteria.

In this thesis a number of models for recommender systems are explored, all using collaborative filtering to produce their recommendations. Extra focus is put on two models: Matrix Factorization, which is a linear model and Multi-Layer Perceptron, which is a non-linear model. With an additional purpose of training the models without collecting any sensitive data from the users, both models were implemented with a learning technique that does not require the server's knowledge of the users' data, called federated learning. The federated version of Matrix Factorization is already well-researched, and has proven not to protect the users' data at all; the data is derivable from the information that the users communicate to the server that is necessary for the learning of the model. However, on the federated Multi-Layer Perceptron model, no research could be found. In this thesis, such a model is therefore designed and presented. Arguments are put forth in support of the privacy preservability of the model, along with a proof of the user data not being analytically derivable for the central server.    In addition, new ways to further put the protection of the users' data on the test are discussed. All models are evaluated on two different data sets. The first data set contains data on ratings of movies and is called MovieLens 1M. The second is a data set that consists of anonymized fund transactions, provided by the Swedish bank SEB for this thesis. Test results suggest that the federated versions of the models can achieve similar recommendation performance as their non-federated counterparts.

Background. With increased pressure from both regulatory bodies and end-users, interest in privacy preserving machine learning methods have increased among companies and researchers in the last few years. One of the main areas of research regarding this is federated learning. Further, with the current situation in the world, interest in cybersecurity is also at an all time high, where intrusion detection systems are one component of interest. With anomaly-based intrusion detection systems using machine learning methods, it is desirable that these can adapt automatically over time as the network patterns change, resulting in online learning being highly relevant for this application. Previous research has studied offline federated intrusion detection systems. However, there have been very little work performed in the study of online federated learning for intrusion detection systems. Objectives. The objective of this thesis is to evaluate the performance of online federated machine learning methods for intrusion detection systems. Furthermore, the thesis will study the performance relationship between offline and online models for both centralized and federated learning, in order to draw conclusions about the ability to extrapolate from results between the different types of models. Methods. This thesis uses a quasi-experiment to evaluate two different types of models, Naive Bayes and Semi-supervised Federated Learning on Evolving Data Streams (SFLEDS), on three different datasets, NSL-KDD, UNSW-NB15, and CIC-IDS2017. For each model, four variants are implemented: centralized offline, centralized online, federated offline and federated online, and in the federated setting the models are evaluated with 20, 30, and 40 clients. Results. The results show that the best performing model in general is the federated online SFLEDS. They also highlight an important problem with using imbalanced datasets without proper care for data preprocessing and model design. Finally, the results show that there are no general relationships between offline and online models that hold in both the centralized and federated settings in terms of prediction performance. Conclusions. The main conclusion of the thesis is that online federated learning has a lot of potential for the application of intrusion detection systems, but more research is required to find the optimal models and parameters that result in satisfactory performance. / Bakgrund. Med ökat tryck från både tillsynsorgan och slutanvändare har intresset för integritetsbevarande maskininlärning ökat hos företag och forskare under de senaste åren. Ett av huvudområdena där det forskas om detta är inom federerad inlärning. Vidare, med det nuvarande läget i världen är intresset för cybersäkerhet högre än någonsin, där bland annat intrångsdetekteringssystem är av intresse. Med avvikelsebaserade intrångsdetekteringssystem som använder sig av maskininlärning så är det önskvärt att dessa automatiskt kan anpassa sig över tid när nätverksmönster förändras, vilket resulterar i att online maskininlärning är högst relevant för området. Tidigare forskning har studerat federerade offline intrångsdetekteringssystem, men det finns väldigt lite forskning gällande federerad online maskininlärning för intrångsdetekteringssystem. Syfte. Syftet med det här arbetet är att utvärdera prestandan av federerad online maskininlärning för intrångsdetekteringssystem. Vidare kommer det här arbetet att studera prestandaförhållandet mellan offline och online modeller för både centraliserad och federerad inlärning, för att kunna dra slutsatser om förmågan att extrapolera resultat mellan olika typer av modeller. \newline\textbf{Metod.} Det här arbetet använder sig av ett kvasiexperiment för att utvärdera två olika modeller, Naive Bayes och Semi-supervised Federated Learning on Evolving Data Streams (SFLEDS), på tre olika dataset, NSL-KDD, UNSW-NB15 och CIC-IDS2017. För varje modell implementeras fyra varianter: centraliserad offline, centraliserad online, federerad offline och federerad online. De federerade modellerna utvärderas med 20, 30 och 40 klienter. Resultat. Resultaten visar att den generellt bästa modellen är online SFLEDS. De belyser även ett viktigt problem med att använda obalanserade dataset utan tillräcklig hänsyn till förbearbetning av datan och modelldesign. Slutligen visar resultaten att det inte finns något generellt samband mellan offline och online modeller som stämmer för både centraliserad och federerad inlärning när det gäller modellprestanda. Slutsatser. Den huvudsakliga slutsatsen från arbetet är att federerad online maskininlärning har stor potential för intrångsdetekteringssystem, men mer forskning krävs för att hitta den bästa modellen och de bästa parametrarna för att nå ett tillfredsställande resultat.

The increasing complexity of telecom networks, induced by the recent development of 5G, is a challenge for detecting faults in the telecom network. In addition to the structural complexity of telecommunication systems, data accessibility has become an issue both in terms of privacy and access cost. We propose a method relying on bi-level Federated Graph Neural Networks to identify anomalies in the telecom network while ensuring reduced communication costs as well as data privacy. Our method considers telecom data as a bi-level graph, where the highest level graph represents the interaction between sites, and each site is further expanded to its software (SW) performance behaviour graph. We developed and compared 4G/5G SW Fault Detection models under 3 settings: (1) Centralized Temporal Graph Neural Networks model: we propose a model to detect anomalies in 4G/5G telecom data. (2) Federated Temporal Graph Neural Networks model: we propose Federated Learning (FL) as a mechanism for privacy-aware training of models for fault detection. (3) Personalized Federated Temporal Graph Neural Networks model: we propose a novel aggregation technique, referred to as FedGraph, leveraging both a graph and the similarities between sites for aggregating the models and proposing models more personalized to each site’s behaviour. We compare the benefits of Federated Learning (FL) models (2) and (3) with centralized training (1) in terms of SW performance data modelling, anomaly detection, and communication cost. The evaluation includes both a scenario with normal functioning sites and a scenario where only a subset of sites exhibit faulty behaviour. The combination of SW execution graphs with GNNs has shown improved modelling performance and minor gains in centralized settings (1). In a normal network context, FL models (2) and (3) perform comparably to centralized training (CL), with slight improvements observed when using the personalized strategy (3). However, in abnormal network scenarios, Federated Learning falls short of achieving comparable detection performance to centralized training. This is due to the unintended learning of abnormal site behaviour, particularly when employing the personalized model (3). These findings highlight the importance of carefully assessing and selecting suitable FL strategies for anomaly detection and model training on telecom network data. / Den ökande komplexiteten i telenäten, som är en följd av den senaste utvecklingen av 5G, är en utmaning när det gäller att upptäcka fel i telenäten. Förutom den strukturella komplexiteten i telekommunikationssystem har datatillgänglighet blivit ett problem både när det gäller integritet och åtkomstkostnader. Vi föreslår en metod som bygger på Federated Graph Neural Networks på två nivåer för att identifiera avvikelser i telenätet och samtidigt säkerställa minskade kommunikationskostnader samt dataintegritet. Vår metod betraktar telekomdata som en graf på två nivåer, där grafen på den högsta nivån representerar interaktionen mellan webbplatser, och varje webbplats utvidgas ytterligare till sin graf för programvarans (SW) prestandabeteende. Vi utvecklade och jämförde 4G/5G SW-feldetekteringsmodeller under 3 inställningar: (1) Central Temporal Graph Neural Networks-modell: vi föreslår en modell för att upptäcka avvikelser i 4G/5G-telekomdata. (2) Federated Temporal Graph Neural Networks-modell: vi föreslår Federated Learning (FL) som en mekanism för integritetsmedveten utbildning av modeller för feldetektering. I motsats till centraliserad inlärning aggregeras lokalt tränade modeller på serversidan och skickas tillbaka till klienterna utan att data läcker ut mellan klienterna och servern, vilket säkerställer integritetsskyddande samarbetsutbildning. (3) Personaliserad Federated Temporal Graph Neural Networks-modell: vi föreslår en ny aggregeringsteknik, kallad FedGraph, som utnyttjar både en graf och likheterna mellan webbplatser för att aggregera modellerna. Vi jämför fördelarna med modellerna Federated Learning (FL) (2) och (3) med centraliserad utbildning (1) när det gäller datamodellering av SW-prestanda, anomalidetektering och kommunikationskostnader. Utvärderingen omfattar både ett scenario med normalt fungerande anläggningar och ett scenario där endast en delmängd av anläggningarna uppvisar felaktigt beteende. Kombinationen av SW-exekveringsgrafer med GNN har visat förbättrad modelleringsprestanda och mindre vinster i centraliserade inställningar (1). I en normal nätverkskontext presterar FL-modellerna (2) och (3) jämförbart med centraliserad träning (CL), med små förbättringar observerade när den personliga strategin används (3). I onormala nätverksscenarier kan Federated Learning dock inte uppnå jämförbar detekteringsprestanda med centraliserad träning. Detta beror på oavsiktlig inlärning av onormalt beteende på webbplatsen, särskilt när man använder den personliga modellen (3). Dessa resultat belyser vikten av att noggrant bedöma och välja lämpliga FL-strategier för anomalidetektering och modellträning på telekomnätdata.

Modern health self-monitoring devices and applications, such as Fitbit and MyFitnessPal, empower users to take concrete actions and set fitness and lifestyle goals based on their recorded trends and statistics. Predicting such trends is beneficial in the road of achieving long-time targets, as the individuals can adjust their diets and habits at any point to guarantee success. The design and implementation of such a system, which also respects user privacy, is the main objective of our work.This application is modelled as a time-series forecasting problem. Given the historical data of users, we aim to predict their eating and lifestyle habits in real-time. We apply the federated learning paradigm to our use-case be- cause of the highly-distributed nature of our data and the privacy concerns of such sensitive recorded information. However, federated learning from het- erogeneous sequences of data can be challenging, as even state-of-the-art ma- chine learning techniques for time-series forecasting can encounter difficulties when learning from very irregular data sequences. Specifically, in the pro- posed healthcare scenario, the machine learning algorithms might fail to cater to users with unique dietary patterns.In this work, we implement a two-step streaming clustering mechanism and group clients that exhibit similar eating and fitness behaviours. The con- ducted experiments prove that learning federatively in this context can achieve very high prediction accuracy, as our predictions are no more than 0.025% far from the ground truth value with respect to the range of each feature. Training separate models for each group of users is shown to be beneficial, especially in terms of the training time, but it is highly dependent on the parameters used for the models and the training process. Our experiments conclude that the configuration used for the general federated model cannot be applied to the clusters of data. However, a decrease in prediction error of more than 45% can be achieved, given the parameters are optimized for each case.Lastly, this work tackles the problem of data privacy by applying state-of- the-art differential privacy techniques. Our empirical study shows that noising the gradients sent to the server is unsuitable for small datasets and cancels out the benefits obtained by prior users’ clustering. On the other hand, noising the training data achieves remarkable results, obtaining a differential privacy level corresponding to an epsilon value of 0.1 with an increase in the observed mean absolute error by a factor of only 0.21. / Moderna apparater och applikationer för självövervakning av hälsa, som Fitbit och MyFitnessPal, ger användarna möjlighet att vidta konkreta åtgärder och sätta fitness- och livsstilsmål baserat på deras dokumenterade trender och statistik. Att förutsäga sådana trender är fördelaktigt för att uppnå långtidsmål, eftersom individerna kan anpassa sina dieter och vanor när som helst för att garantera framgång.Utformningen och implementeringen av ett sådant system, som dessutom respekterar användarnas integritet, är huvudmålet för vårt arbete. Denna appli- kation är modellerad som ett tidsserieprognosproblem. Med avseende på an- vändarnas historiska data är målet att förutsäga deras matvanor och livsstilsva- nor i realtid. Vi tillämpar det federerade inlärningsparadigmet på vårt använd- ningsfall på grund av den mycket distribuerade karaktären av vår data och in- tegritetsproblemen för sådan känslig bokförd information. Federerade lärande från heterogena datasekvenser kan emellertid vara utmanande, eftersom även de modernaste maskininlärningstekniker för tidsserieprognoser kan stöta på svårigheter när de lär sig från mycket oregelbundna datasekvenser. Specifikt i det föreslagna sjukvårdsscenariot kan maskininlärningsalgoritmerna misslyc- kas med att förse användare med unika dietmönster.I detta arbete implementerar vi en tvåstegsströmmande klustermekanism och grupperar användare som uppvisar liknande ät- och fitnessbeteenden. De genomförda experimenten visar att federerade lärande i detta sammanhang kan uppnå mycket hög nogrannhet i förutsägelse, eftersom våra förutsägelser in- te är mer än 0,025% ifrån det sanna värdet med avseende på intervallet för varje funktion. Träning av separata modeller för varje grupp användare visar sig vara fördelaktigt, särskilt gällande träningstiden, men det är mycket be- roende av parametrarna som används för modellerna och träningsprocessen. Våra experiment drar slutsatsen att konfigurationen som används för den all- männa federerade modellen inte kan tillämpas på dataklusterna. Dock kan en minskning av förutsägelsefel på mer än 45% uppnås, givet att parametrarna är optimerade för varje fall.Slutligen hanteras problemet med datasekretess genom att tillämpa bästa tillgängliga differentiell integritetsteknik. Vår empiriska studie visar att adde- ra brus till gradienter som skickas till servern är olämpliga för liten data och avbryter fördelarna med tidigare användares kluster. Däremot, genom att ad- dera brus till träningsdata uppnås anmärkningsvärda resultat. En differentierad integritetsnivå motsvarande ett epsilonvärde på 0,1 med en ökning av det ob- serverade genomsnittliga absoluta felet med en faktor på endast 0,21 erhölls.

<p>Over the past decade, distributed representation learning has emerged as a popular alternative to conventional centralized machine learning training. The increasing interest in distributed representation learning, specifically federated learning, can be attributed to its fundamental property that promotes data privacy and communication savings. While conventional ML encourages aggregating data at a central location (e.g., data centers), distributed representation learning advocates keeping data at the source and instead transmitting model parameters across the network. However, since the advent of deep learning, model sizes have become increasingly large often comprising million-billions of parameters, which leads to the problem of communication latency in the learning process. In this thesis, we propose to tackle the problem of communication latency in two different ways: (i) learning private representation of data to enable its sharing, and (ii) reducing the communication latency by minimizing the corresponding long-range communication requirements.</p> <p><br></p> <p>To tackle the former goal, we first start by studying the problem of learning representations that are private yet informative, i.e., providing information about intended ''ally'' targets while hiding sensitive ''adversary'' attributes. We propose Exclusion-Inclusion Generative Adversarial Network (EIGAN), a generalized private representation learning (PRL) architecture that accounts for multiple ally and adversary attributes, unlike existing PRL solutions. We then address the practical constraints of the distributed datasets by developing Distributed EIGAN (D-EIGAN), the first distributed PRL method that learns a private representation at each node without transmitting the source data. We theoretically analyze the behavior of adversaries under the optimal EIGAN and D-EIGAN encoders and the impact of dependencies among ally and adversary tasks on the optimization objective. Our experiments on various datasets demonstrate the advantages of EIGAN in terms of performance, robustness, and scalability. In particular, EIGAN outperforms the previous state-of-the-art by a significant accuracy margin (47% improvement), and D-EIGAN's performance is consistently on par with EIGAN under different network settings.</p> <p><br></p> <p>We next tackle the latter objective - reducing the communication latency - and propose two timescale hybrid federated learning (TT-HF), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. In TT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of O(1/t) while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions.</p>