This work introduces the available authorization frameworks for the purpose of Single Sign-On functionality within an enterprise, along with the fundamental technicalities. The focus of the work is on SAML 2.0 and OAuth 2.0 frame- works. Following the details related to available protocol flows, supported client profiles and security considerations, the two frameworks are compared in accordance with a set of factors given in a criteria. The report discusses the possibilities provided by a Microsoft Windows based infrastructure, as well as different scenarios and their feasibility in an enterprise environment. The preferred framework, OAuth 2.0, is selected according to the given criteria and the comparative discussions.

Cette thèse s'intéresse à la production et au partage cohérent de connaissances distribuées dans le domaine des sciences de la vie. Malgré l'augmentation constante des capacités de stockage et de calcul des infrastructures informatiques, les approches centralisées pour la gestion de grandes masses de données scientifiques multi-sources deviennent inadaptées pour plusieurs raisons: (i) elles ne garantissent pas l'autonomie des fournisseurs de données qui doivent conserver un certain contrôle sur les données hébergées pour des raisons éthiques et/ou juridiques, (ii) elles ne permettent pas d'envisager le passage à l'échelle des plateformes en sciences computationnelles qui sont la source de productions massives de données scientifiques. Nous nous intéressons, dans le contexte des plateformes collaboratives en sciences de la vie NeuroLOG et VIP, d'une part, aux problématiques de distribution et d'hétérogénéité sous-jacentes au partage de ressources, potentiellement sensibles ; et d'autre part, à la production automatique de connaissances au cours de l'usage de ces plateformes, afin de faciliter l'exploitation de la masse de données produites. Nous nous appuyons sur une approche ontologique pour la modélisation des connaissances et proposons à partir des technologies du web sémantique (i) d'étendre ces plateformes avec des stratégies efficaces, statiques et dynamiques, d'interrogations sémantiques fédérées et (ii) d'étendre leur environnent de traitement de données pour automatiser l'annotation sémantique des résultats d'expérience ``in silico'', à partir de la capture d'informations de provenance à l'exécution et de règles d'inférence spécifiques au domaine. Les résultats de cette thèse, évalués sur l'infrastructure distribuée et contrôlée Grid'5000, apportent des éléments de réponse à trois enjeux majeurs des plateformes collaboratives en sciences computationnelles : (i) un modèle de collaborations sécurisées et une stratégie de contrôle d'accès distribué pour permettre la mise en place d'études multi-centriques dans un environnement compétitif, (ii) des résumés sémantiques d'expérience qui font sens pour l'utilisateur pour faciliter la navigation dans la masse de données produites lors de campagnes expérimentales, et (iii) des stratégies efficaces d'interrogation et de raisonnement fédérés, via les standards du Web Sémantique, pour partager les connaissances capitalisées dans ces plateformes et les ouvrir potentiellement sur le Web de données. Mots-clés: Flots de services et de données scientifiques, Services web sémantiques, Provenance, Web de données, Web sémantique, Fédération de bases de connaissances, Intégration de données distribuées, e-Sciences, e-Santé. / This thesis addresses the issues of coherent distributed knowledge production and sharing in the Life-science area. In spite of the continuously increasing computing and storage capabilities of computing infrastructures, the management of massive scientific data through centralized approaches became inappropriate, for several reasons: (i) they do not guarantee the autonomy property of data providers, constrained, for either ethical or legal concerns, to keep the control over the data they host, (ii) they do not scale and adapt to the massive scientific data produced through e-Science platforms. In the context of the NeuroLOG and VIP Life-science collaborative platforms, we address on one hand, distribution and heterogeneity issues underlying, possibly sensitive, resource sharing ; and on the other hand, automated knowledge production through the usage of these e-Science platforms, to ease the exploitation of the massively produced scientific data. We rely on an ontological approach for knowledge modeling and propose, based on Semantic Web technologies, to (i) extend these platforms with efficient, static and dynamic, transparent federated semantic querying strategies, and (ii) to extend their data processing environment, from both provenance information captured at run-time and domain-specific inference rules, to automate the semantic annotation of ``in silico'' experiment results. The results of this thesis have been evaluated on the Grid'5000 distributed and controlled infrastructure. They contribute to addressing three of the main challenging issues faced in the area of computational science platforms through (i) a model for secured collaborations and a distributed access control strategy allowing for the setup of multi-centric studies while still considering competitive activities, (ii) semantic experiment summaries, meaningful from the end-user perspective, aimed at easing the navigation into massive scientific data resulting from large-scale experimental campaigns, and (iii) efficient distributed querying and reasoning strategies, relying on Semantic Web standards, aimed at sharing capitalized knowledge and providing connectivity towards the Web of Linked Data.

De plus en plus de sources de données liées sont publiées à travers le Web en s'appuyant sur les technologies du Web sémantique, formant ainsi un large réseau de données distribuées. Cependant il est difficile pour les consommateurs de données de profiter de la richesse de ces données, compte tenu de leur distribution, de l'augmentation de leur volume et de l'autonomie des sources de données. Les moteurs fédérateurs de données permettent d'interroger ces sources de données en utilisant des techniques de traitement de requêtes distribuées. Cependant, une mise en œuvre naïve de ces techniques peut générer un nombre considérable de requêtes distantes et de nombreux résultats intermédiaires entraînant ainsi un long temps de traitement des requêtes et des communications réseau coûteuse. Par ailleurs, la sémantique des requêtes distribuées est souvent ignorée. L'expressivité des requêtes, le partitionnement des données et leur réplication sont d'autres défis auxquels doivent faire face les moteurs de requêtes. Pour répondre à ces défis, nous avons d'abord proposé une sémantique des requêtes distribuées compatible avec les standards SPARQL et RDF qui préserve l’expressivité de SPARQL. Nous avons ensuite présenté plusieurs stratégies d'optimisation pour un moteur de requêtes fédérées qui interroge de manière transparente des sources de données distribuées. La performance de ces optimisations est évaluée sur une implémentation d’un moteur de requêtes distribuées SPARQL / Driven by the Semantic Web standards, an increasing number of RDF data sources are published and connected over the Web by data providers, leading to a large distributed linked data network. However, exploiting the wealth of these data sources is very challenging for data consumers considering the data distribution, their volume growth and data sources autonomy. In the Linked Data context, federation engines allow querying these distributed data sources by relying on Distributed Query Processing (DQP) techniques. Nevertheless, a naive implementation of the DQP approach may generate a tremendous number of remote requests towards data sources and numerous intermediate results, thus leading to costly network communications. Furthermore, the distributed query semantics is often overlooked. Query expressiveness, data partitioning, and data replication are other challenges to be taken into account. To address these challenges, we first proposed in this thesis a SPARQL and RDF compliant Distributed Query Processing semantics which preserves the SPARQL language expressiveness. Afterwards, we presented several strategies for a federated query engine that transparently addresses distributed data sources, while managing data partitioning, query results completeness, data replication, and query processing performance. We implemented and evaluated our approach and optimization strategies in a federated query engine to prove their effectiveness.

<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>

The use of Machine Learning (ML) in business has increased significantly over the past years. Creating high quality and robust models requires a lot of data, which is at times infeasible to obtain. As more people are becoming concerned about their data being misused, data privacy is increasingly strengthened. In 2018, the General Data Protection Regulation (GDPR), was announced within the EU. Models that use either sensitive or personal data to train need to obtain that data in accordance with the regulatory rules, such as GDPR. One other data related issue is that enterprises who wish to collaborate on model building face problems when it requires them to share their private corporate data [36, 38]. In this thesis we will investigate how one might overcome the issue of directly accessing private data when training ML models by employing Federated Learning (FL) [38]. The concept of FL is to allow several silos, i.e. separate parties, to train models with the same objective, using their local data and then with the learned model parameters create a central model. The objective of the central model is to obtain the information learned by the separate models, without ever accessing the raw data itself. This is achieved by averaging the separate models’ weights into the central model. FL thus facilitates opportunities to train a model on large amounts of data from several sources, without the need of having access to the data itself. If one can create a model with this methodology, that is not significantly worse than a model trained on the raw data, then positive effects such as strengthened data privacy, cross-enterprise collaboration and more could be attainable. In this work we have used a financial data set consisting of 25242 equity research reports, provided by Skandinaviska Enskilda Banken (SEB). Each report has a recommendation label, either Buy, Sell or Hold, making this a multi-class classification problem. To evaluate the feasibility of FL we fine-tune the pre-trained Transformer model AlbertForSequenceClassification [37] on the classification task. We create one baseline model using the entire data set and an FL model with different experimental settings, for which the data is distributed both uniformly and non-uniformly. The baseline model is used to benchmark the FL model. Our results indicate that the best FL setting only suffers a small reduction in performance. The baseline model achieves an accuracy of 83.5% compared to 82.8% for the best FL model setting. Further, we find that with an increased number of clients, the performance is worsened. We also found that our FL model was not sensitive to non-uniform data distributions. All in all, we show that FL results in slightly worse generalisation compared to the baseline model, while strongly improving on data privacy, as the central model never accesses the clients’ data. / Företags nyttjande av maskininlärning har de senaste åren ökat signifikant och för att kunna skapa högkvalitativa modeller krävs stora mängder data, vilket kan vara svårt att insamla. Parallellt med detta så ökar också den allmänna förståelsen för hur användandet av data missbrukas, vilket har lätt till ett ökat behov av starkare datasäkerhet. 2018 så trädde General Data Protection Regulation (GDPR) i kraft inom EU, vilken bland annat ställer krav på hur företag skall hantera persondata. Företag med maskininlärningsmodeller som på något sätt använder känslig eller personlig data behöver således ha fått tillgång till denna data i enlighet med de rådande lagar och regler som omfattar datahanteringen. Ytterligare ett datarelaterat problem är då företag önskar att skapa gemensamma maskininlärningsmodeller som skulle kräva att de delar deras bolagsdata [36, 38]. Denna uppsats kommer att undersöka hur Federerad Inlärning [38] kan användas för att skapa maskinlärningsmodeller som överkommer dessa datasäkerhetsrelaterade problem. Federerad Inlärning är en metod för att på ett decentraliserat vis träna maskininlärningsmodeller. Detta omfattar att låta flera aktörer träna en modell var. Varje enskild aktör tränar respektive modell på deras isolerade data och delar sedan endast modellens parametrar till en central modell. På detta vis kan varje enskild modell bidra till den gemensamma modellen utan att den gemensamma modellen någonsin haft tillgång till den faktiska datan. Givet att en modell, skapad med Federerad Inlärning kan uppnå liknande resultat som en modell tränad på rådata, så finns många positiva fördelar så som ökad datasäkerhet och ökade samarbeten mellan företag. Under arbetet har ett dataset, bestående av 25242 finansiella rapporter tillgängliggjort av Skandinaviska Ensilda Banken (SEB) använts. Varje enskild rapport innefattar en rekommendation, antingen Köp, Sälj eller Håll, vilket innebär att vi utför muliklass-klassificering. Med datan tränas den förtränade Transformermodellen AlbertForSequence- Classification [37] på att klassificera rapporterna. En Baseline-modell, vilken har tränats på all rådata och flera Federerade modellkonfigurationer skapades, där bland annat varierande fördelningen av data mellan aktörer från att vara jämnt fördelat till vara ojämnt fördelad. Resultaten visar att den bästa Federerade modellkonfigurationen endast presterar något sämre än Baseline-modellen. Baselinemodellen uppnådde en klassificeringssäkerhet på 83.5% medan den bästa Federerade modellen uppnådde 82.8%. Resultaten visar också att den Federerade modellen inte var känslig mot att variera fördelningen av datamängd mellan aktorerna, samt att med ett ökat antal aktörer så minskar klassificeringssäkerheten. Sammanfattningsvis så visar vi att Federerad Inlärning uppnår nästan lika goda resultat som Baseline-modellen, samtidigt så bidrar metoden till avsevärt bättre datasäkerhet då den centrala modellen aldrig har tillgång till rådata.

Federated learning has gained attention recently for its ability to protect data privacy and distribute computing loads [1]. It overcomes the limitations of traditional machine learning algorithms by allowing computers to train on remote data inputs and build models while keeping participant privacy intact. Traditional machine learning offered a solution by enabling computers to learn patterns and make decisions from data without explicit programming. It opened up new possibilities for automating tasks, recognizing patterns, and making predictions. With the exponential growth of data and advances in computational power, machine learning has become a powerful tool in various domains, driving innovations in fields such as image recognition, natural language processing, autonomous vehicles, and personalized recommendations. traditional machine learning, data is usually transferred to a central server, raising concerns about privacy and security. Centralizing data exposes sensitive information, making it vulnerable to breaches or unauthorized access. Centralized machine learning assumes that all data is available at a central location, which is only sometimes practical or feasible. Some data may be distributed across different locations, owned by different entities, or subject to legal or privacy restrictions. Training a global model in traditional machine learning involves frequent communication between the central server and participating devices. This communication overhead can be substantial, particularly when dealing with large-scale datasets or resource-constrained devices. / Recent studies have uncovered security issues with most of the federated learning models. One common false assumption in the federated learning model is that participants are the attacker and would not use polluted data. This vulnerability enables attackers to train their models using polluted data and then send the polluted updates to the training server for aggregation, potentially poisoning the overall model. In such a setting, it is challenging for an edge server to thoroughly inspect the data used for model training and supervise any edge device. This study evaluates the vulnerabilities present in federated learning and explores various types of attacks that can occur. This paper presents a robust prevention scheme to address these vulnerabilities. The proposed prevention scheme enables federated learning servers to monitor participants actively in real-time and identify infected individuals by introducing an encrypted verification scheme. The paper outlines the protocol design of this prevention scheme and presents experimental results that demonstrate its effectiveness. / Thesis / Doctor of Philosophy (PhD) / federated learning models face significant security challenges and can be vulnerable to attacks. For instance, federated learning models assume participants are not attackers and will not manipulate the data. However, in reality, attackers can compromise the data of remote participants by inserting fake or altering existing data, which can result in polluted training results being sent to the server. For instance, if the sample data is an animal image, attackers can modify it to contaminate the training data. This paper introduces a robust preventive approach to counter data pollution attacks in real-time. It incorporates an encrypted verification scheme into the federated learning model, preventing poisoning attacks without the need for specific attack detection programming. The main contribution of this paper is a mechanism for detection and prevention that allows the training server to supervise real-time training and stop data modifications in each client's storage before and between training rounds. The training server can identify real-time modifications and remove infected remote participants with this scheme.

[pt] As redes da Internet das Coisas (IdC) incluem dispositivos inteligentes que contêm muitos sensores que permitem interagir com o mundo físico, coletando e processando dados de streaming em tempo real. O consumo total de energia e o custo desses sensores afetam o consumo de energia e o custo dos dispositivos IdC. O tipo de sensor determina a precisão da interface analógica e a resolução dos conversores analógico-digital (ADCs). A resolução dos ADCs tem um compromisso entre a precisão de inferência e o consumo de energia, uma vez que o consumo de energia dos ADCs depende do número de bits usados para representar amostras digitais. Nesta tese, apresentamos um esquema de aprendizado distribuído com eficiência energética usando sinais quantizados para redes da IdC. Em particular, desenvolvemos algoritmos de gradiente estocástico com reconhecimento de quantização distribuído (DQA-LMS) e de mínimos quadrados recursivos com reconhecimento de quantização distribuído (DQA-RLS) que podem aprender parâmetros de maneira eficiente em energia usando sinais quantizados com poucos bits, exigindo um baixo custo computacional. Além disso, desenvolvemos uma estratégia de compensação de viés para melhorar ainda mais o desempenho dos algoritmos propostos. Uma análise estatística dos algoritmos propostos juntamente com uma avaliação da complexidade computacional das técnicas propostas e existentes é realizada. Os resultados numéricos avaliam os algoritmos com reconhecimento de quantização distribuída em relação às técnicas existentes para uma tarefa de estimação de parâmetros em que os dispositivos IdC operam em um modo ponto a ponto. Também apresentamos um esquema de aprendizado federativo com eficiência energética usando sinais quantizados para redes de IdC. Desenvolvemos o algoritmo federated averaging LMS (QA-FedAvg-LMS) com reconhecimento de quantização para redes IdC estruturadas por configuração de aprendizado federativo em que os dispositivos IdC trocam suas estimativas com um servidor. Uma estratégia de compensação de viés para QA-FedAvg-LMS é proposta junto com sua análise estatística e a avaliação de desempenho em relação às técnicas existentes com resultados numéricos. / [en] Internet of Things (IoT) networks include smart devices that contain many sensors that allow them to interact with the physical world, collecting and processing streaming data in real time. The total energy-consumption and cost of these sensors affect the energy-consumption and the cost of IoT devices. The type of sensor determines the accuracy of the analog interface and the resolution of the analog-to-digital converters (ADCs). The ADC resolution requirement has a trade-off between sensing performance and energy consumption since the energy consumption of ADCs strongly depends on the number of bits used to represent digital samples. In this thesis, we present an energy-efficient distributed learning framework using coarsely quantized signals for IoT networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) and a distributed quantization-aware recursive least-squares (DQA-RLS) algorithms that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. Moreover, we develop a bias compensation strategy to further improve the performance of the proposed algorithms. We then carry out a statistical analysis of the proposed algorithms along with a computational complexity evaluation of the proposed and existing techniques. Numerical results assess the distributed quantization-aware algorithms against existing techniques for distributed parameter estimation where IoT devices operate in a peer-to-peer mode. We also introduce an energy-efficient federated learning framework using coarsely quantized signals for IoT networks, where IoT devices exchange their estimates with a server. We then develop the quantization-aware federated averaging LMS (QA-FedAvg-LMS) algorithm to perform parameter estimation at the clients and servers. Furthermore, we devise a bias compensation strategy for QA-FedAvg-LMS, carry out its statistical analysis, and assess its performance against existing techniques with numerical results.

Lite-Agro studies applications of TinyML in pear (Pyrus communis) tree disease identification and explores hardware implementations with an ESP32 microcontroller. The study works with the DiaMOS Pear Dataset to learn through image analysis whether the leaf is healthy or not, and classifies it according to curl, healthy, spot or slug categories. The system is designed as a low cost and light-duty computing detection edge solution that compares models such as InceptionV3, XceptionV3, EfficientNetB0, and MobileNetV2. This work also researches integration with federated learning frameworks and provides an introduction to federated averaging algorithms.

O federalismo conhece, na experiência moderna, diversas formações e conformações, segundo a ideologia que o permeia e a necessidade histórica que o explica e que o implica. Embora não seja possível falar em um modelo puro ou autêntico de federalismo, há uma característica que lhe é essencial, cuja falta negaria sua própria razão de ser: a coexistência, sob o mesmo poder soberano, de duas ou mais sociedades políticas dotadas de estatalidade. A estatalidade é informada pela existência de um poder político de inaugurar determinada ordem jurídica. No Estado dito unitário, trata-se da soberania. No Estado dito federativo, a soberania convive com o poder político dos Estados federados - a autonomia federativa. Assim como a soberania, a autonomia federativa é um poder político constituinte, mas, ao contrário daquela, é também poder político constituído (competência), limitado pelo poder soberano. A autonomia federativa implica, ainda, a competência para constituir competências políticas e governamentais. Tais limites são postos pelo poder soberano na Constituição do Estado federativo, que define o grau de autonomia federativa. Esse poder tem sofrido oscilações ao longo das Constituições republicanas brasileiras, havendo, em todas elas, considerável disparidade entre a autonomia federativa formal (que o texto revela) e a autonomia federativa real (que se pratica), causada, sobretudo, pelo antagonismo dos interesses políticos e econômicos que determinam, em última instância, a descentralização político-governamental. A presente tese propõe a conceituação e a delimitação da autonomia federativa formal no Direito Constitucional brasileiro posto pela Constituição da República Federativa do Brasil de 1988 / Federalism has had, in the modern experience, different frames and meanings, according to the ideology embedded into it and the historical necessity that explains and implies it. Although it is not possible to advocate a pure or authentic model for federalism, there is an essential feature, whose absence would deny its own reason for being: the coexistence, under the same sovereign power, of two or more political societies with statehood. Statehood is constituted by a political power capable to create a particular legal order. In so-called unitary states, such political power is the sovereignty. In federal states, the sovereignty of nation-state coexists with the political power of federated states - the federative autonomy. Like sovereignty, federative autonomy is a constitutional-political power. However, in contrast to the former, federative autonomy is also constituted political power (competence), limited by the sovereign power. The federative autonomy also implies the competence to establish political and governmental powers. These limits are set by the sovereign power in the Constitution of the federal state, which defines the degree of federative autonomy. Such power has oscillated along the Brazilian republican constitutions. All of them revealed considerable disparity between the formal federative autonomy (which the legal text provides) and the real federative autonomy (which is practiced), which was caused, mainly, by the antagonism between political and economic interests. Such interests ultimately determine political and governmental decentralization. This dissertation advocates the conceptualization and delimitation of formal federative autonomy in the Brazilian Constitutional Law set forth by the Constitution of the Federative Republic of Brazil.

O federalismo conhece, na experiência moderna, diversas formações e conformações, segundo a ideologia que o permeia e a necessidade histórica que o explica e que o implica. Embora não seja possível falar em um modelo puro ou autêntico de federalismo, há uma característica que lhe é essencial, cuja falta negaria sua própria razão de ser: a coexistência, sob o mesmo poder soberano, de duas ou mais sociedades políticas dotadas de estatalidade. A estatalidade é informada pela existência de um poder político de inaugurar determinada ordem jurídica. No Estado dito unitário, trata-se da soberania. No Estado dito federativo, a soberania convive com o poder político dos Estados federados - a autonomia federativa. Assim como a soberania, a autonomia federativa é um poder político constituinte, mas, ao contrário daquela, é também poder político constituído (competência), limitado pelo poder soberano. A autonomia federativa implica, ainda, a competência para constituir competências políticas e governamentais. Tais limites são postos pelo poder soberano na Constituição do Estado federativo, que define o grau de autonomia federativa. Esse poder tem sofrido oscilações ao longo das Constituições republicanas brasileiras, havendo, em todas elas, considerável disparidade entre a autonomia federativa formal (que o texto revela) e a autonomia federativa real (que se pratica), causada, sobretudo, pelo antagonismo dos interesses políticos e econômicos que determinam, em última instância, a descentralização político-governamental. A presente tese propõe a conceituação e a delimitação da autonomia federativa formal no Direito Constitucional brasileiro posto pela Constituição da República Federativa do Brasil de 1988 / Federalism has had, in the modern experience, different frames and meanings, according to the ideology embedded into it and the historical necessity that explains and implies it. Although it is not possible to advocate a pure or authentic model for federalism, there is an essential feature, whose absence would deny its own reason for being: the coexistence, under the same sovereign power, of two or more political societies with statehood. Statehood is constituted by a political power capable to create a particular legal order. In so-called unitary states, such political power is the sovereignty. In federal states, the sovereignty of nation-state coexists with the political power of federated states - the federative autonomy. Like sovereignty, federative autonomy is a constitutional-political power. However, in contrast to the former, federative autonomy is also constituted political power (competence), limited by the sovereign power. The federative autonomy also implies the competence to establish political and governmental powers. These limits are set by the sovereign power in the Constitution of the federal state, which defines the degree of federative autonomy. Such power has oscillated along the Brazilian republican constitutions. All of them revealed considerable disparity between the formal federative autonomy (which the legal text provides) and the real federative autonomy (which is practiced), which was caused, mainly, by the antagonism between political and economic interests. Such interests ultimately determine political and governmental decentralization. This dissertation advocates the conceptualization and delimitation of formal federative autonomy in the Brazilian Constitutional Law set forth by the Constitution of the Federative Republic of Brazil.