Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyses assume static optimization problems and offer no guarantees in the presence of drifts in the problem solution or data characteristics. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm. The results clarify the trade-off between convergence and tracking performance.
When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded. To encourage the application of federated learning, this paper employs a management strategy, i.e., more contributions should lead to more rewards. We propose a novel hierarchically fair federated learning (HFFL) framework. Under this framework, agents are rewarded in proportion to their pre-negotiated contribution levels. HFFL+ extends this to incorporate heterogeneous models. Theoretical analysis and empirical evaluation on several…
Federated learning enables the development of a machine learning model among collaborating agents without requiring them to share their underlying data. However, malicious agents who train on random data, or worse, on datasets with the result classes inverted, can weaken the combined model. BlockFLow is an accountable federated learning system that is fully decentralized and privacy-preserving. Its primary goal is to reward agents proportional to the quality of their contribution while protecting the privacy of the underlying datasets and being resilient to malicious adversaries. Specifically,…
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Federated learning encapsulates distributed learning strategies that are managed by a central unit. Since it relies on using a selected number of agents at each iteration, and since each agent, in turn, taps into its local data, it is only natural to study optimal sampling policies for selecting agents and their data in federated learning implementations. Usually, only uniform sampling schemes are used. However, in this work, we examine the effect of importance sampling and devise schemes for sampling agents and data non-uniformly guided by a performance measure. We find that in schemes involv…
In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model. Standard federated learning algorithms applied to this setting are vulnerable to Byzantine attacks on even a small fraction of agents. We propose a novel algorithm with a robust aggregation oracle that utilizes the geometric median. We prove that our proposed algorithm is robust to Byzantine attacks on fewer than half of agents and achieves a sublinear $\tilde{\mathcal{O}}({T^{3/4}})$ regret with $\mathcal{O}(\sqr…
Hainan University（海南大学）；University of Macao（澳门大学）；The Hong Kong Polytechnic University（香港理工大学）
Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every r…
Princeton University（普林斯顿大学）；I.B.M T.J. Watson Research Center（IBM托马斯·J·沃森研究中心）
Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server. In this work, we explore the threat of model poisoning attacks on federated learning initiated by a single, non-colluding malicious agent where the adversarial objective is to cause the model to misclassify a set of chosen inputs with high confidence. We explore a number of strategies to carry out this attack, starting with simple boosting of the malicious agent's upda…
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Federated learning involves a mixture of centralized and decentralized processing tasks, where a server regularly selects a sample of the agents and these in turn sample their local data to compute stochastic gradients for their learning updates. This process runs continually. The sampling of both agents and data is generally uniform; however, in this work we consider non-uniform sampling. We derive optimal importance sampling strategies for both agent and data selection and show that non-uniform sampling without replacement improves the performance of the original FedAvg algorithm. We run exp…
Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference within the distributed and federated learning (DL and FL) frameworks, the central server needs to process a large amount of data while relying on various agents to perform multiple distributed training tasks. Considering the decentralized computing topology, privacy has become a first-class concern. Moreover, assuming limited information processing capability for the agents calls for a sophisticated \textit{privacy-preser…
Zhejiang University/University of Illinois at Urbana–Champaign Institute（浙江大学伊利诺伊大学厄巴纳-香槟校区联合学院）；Zhejiang University（浙江大学）；Northwestern University（西北大学）
Federated learning enables machine learning algorithms to be trained over a network of multiple decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents' incentives to voluntarily participate and obediently follow traditional federated learning alg…
Toyota Technological Institute at Chicago（芝加哥丰田理工学院）；University of California, Berkeley（加州大学伯克利分校）；Cornell University（康奈尔大学）
In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents. However, little is known about how collaboration protocols should take agents' incentives into account when allocating individual resources for communal learning in order to maintain such collaborations. Inspired by game theoretic notions, this paper introduces a framework for incentive-aware learning and data sharing in federated learning. Our stable and envy-free equilibria capture notions of collaboration in the presence of agents interested in m…
University of Illinois（伊利诺伊大学）；I.B.M T.J. Watson Research Center（IBM TJ·沃森研究中心）
Federated learning enables model training over a distributed corpus of agent data. However, the trained model is vulnerable to adversarial examples, designed to elicit misclassification. We study the feasibility of using adversarial training (AT) in the federated learning setting. Furthermore, we do so assuming a fixed communication budget and non-iid data distribution between participating agents. We observe a significant drop in both natural and adversarial accuracies when AT is used in the federated setting as opposed to centralized training. We attribute this to the number of epochs of AT …
A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learning
Department of Industrial and Systems Engineering, Rutgers University（工业与系统工程系，罗杰斯大学）；Department of Electrical and Computer Engineering, Rutgers University（电气与计算机工程系，罗杰斯大学）
Booth School of Business, The University of Chicago（芝加哥大学布斯商学院）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究院）
We consider the problem of personalized federated learning when there are known cluster structures within users. An intuitive approach would be to regularize the parameters so that users in the same cluster share similar model weights. The distances between the clusters can then be regularized to reflect the similarity between different clusters of users. We develop an algorithm that allows each cluster to communicate independently and derive the convergence results. We study a hierarchical linear model to theoretically demonstrate that our approach outperforms agents learning independently an…
Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks
London South Bank University（伦敦南岸大学）；Instituto de Telecomunicações（电信研究所）
University of Illinois at Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）
Federated learning provides an effective paradigm to jointly optimize a model benefited from rich distributed data while protecting data privacy. Nonetheless, the heterogeneity nature of distributed data makes it challenging to define and ensure fairness among local agents. For instance, it is intuitively "unfair" for agents with data of high quality to sacrifice their performance due to other agents with low quality data. Currently popular egalitarian and weighted equity-based fairness measures suffer from the aforementioned pitfall. In this work, we aim to formally represent this problem and…
Media Lab（媒体实验室）；Institute for Data, Systems and Society（数据、系统与社会研究所）；Massachusetts Institute of Technology（麻省理工学院）
The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning. In this paper, we study this in context of the contextual linear bandit: we consider a collection of agents cooperating to solve a common contextual bandit, while ensuring that their communication remains private. For this problem, we devise \textsc{FedUCB}, a multiagent private algorithm for both centralized and decentralized (peer-to-peer) federated learning. We provide a rigorous technical analysis of its utility in terms of regret, improving several results in cooper…
City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
In this paper, we are interested in what we term the federated private bandits framework, that combines differential privacy with multi-agent bandit learning. We explore how differential privacy based Upper Confidence Bound (UCB) methods can be applied to multi-agent environments, and in particular to federated learning environments both in `master-worker' and `fully decentralized' settings. We provide a theoretical analysis on the privacy and regret performance of the proposed methods and explore the tradeoffs between these two.
Federated learning is typically considered a beneficial technology which allows multiple agents to collaborate with each other, improve the accuracy of their models, and solve problems which are otherwise too data-intensive / expensive to be solved individually. However, under the expectation that other agents will share their data, rational agents may be tempted to engage in detrimental behavior such as free-riding where they contribute no data but still enjoy an improved model. In this work, we propose a framework to analyze the behavior of such rational data generators. We first show how a …
Intelligent Agents for Auction-based Federated Learning: A Survey
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；Department of Computer Science, Bar-Ilan University（巴伊兰大学计算机科学系）
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Stony Brook University（石溪大学）
In this paper, we study \emph{Federated Bandit}, a decentralized Multi-Armed Bandit problem with a set of $N$ agents, who can only communicate their local data with neighbors described by a connected graph $G$. Each agent makes a sequence of decisions on selecting an arm from $M$ candidates, yet they only have access to local and potentially biased feedback/evaluation of the true reward for each action taken. Learning only locally will lead agents to sub-optimal actions while converging to a no-regret strategy requires a collection of distributed data. Motivated by the proposal of federated le…
Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing
Division of Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway（挪威癌症登记处，挪威公共卫生研究所，奥斯陆）；Department of Physics and Technology, The Arctic University of Norway, Tromsø, Norway（挪威极地大学物理与技术系）
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning
Faculty of Mathematics and Information Science, Warsaw University of Technology（华沙理工大学数学与信息科学学院）；Distributed Systems Group, TU Wien（维也纳技术大学分布式系统组）；Universitat Pompeu Fabra（庞培法布拉大学）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）
In many real-world situations, data is distributed across multiple self-interested agents. These agents can collaborate to build a machine learning model based on data from multiple agents, potentially reducing the error each experiences. However, sharing models in this way raises questions of fairness: to what extent can the error experienced by one agent be significantly lower than the error experienced by another agent in the same coalition? In this work, we consider two notions of fairness that each may be appropriate in different circumstances: "egalitarian fairness" (which aims to bound …
Federated Learning (FL) is a decentralized machine learning protocol that allows a set of participating agents to collaboratively train a model without sharing their data. This makes FL particularly suitable for settings where data privacy is desired. However, it has been observed that the performance of FL is closely tied with the local data distributions of agents. Particularly, in settings where local data distributions vastly differ among agents, FL performs rather poorly with respect to the centralized training. To address this problem, we hypothesize the reasons behind the performance de…
Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives
School of Intelligent Systems Engineering, Sun Yat-Sen University（中山大学智能系统工程学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）
University of California, Los Angeles（加州大学洛杉矶分校）；Yale University（耶鲁大学）
We study federated contextual linear bandits, where $M$ agents cooperate with each other to solve a global contextual linear bandit problem with the help of a central server. We consider the asynchronous setting, where all agents work independently and the communication between one agent and the server will not trigger other agents' communication. We propose a simple algorithm named \texttt{FedLinUCB} based on the principle of optimism. We prove that the regret of \texttt{FedLinUCB} is bounded by $\tilde{O}(d\sqrt{\sum_{m=1}^M T_m})$ and the communication complexity is $\tilde{O}(dM^2)$, where…
