Federated Learning (FL) enables statistical models to be built on user-generated data without compromising data security and user privacy. For this reason, FL is well suited for on-device learning from mobile devices where data is abundant and highly privatized. Constrained by the temporal availability of mobile devices, only a subset of devices is accessible to participate in the iterative protocol consisting of training and aggregation. In this study, we take a step toward better understanding the effect of non-independent data distributions arising from block-cyclic sampling. By conducting extensive experiments on visual classification, we measure the effects of block-cyclic sampling (both standalone and in combination with non-balanced block distributions). Specifically, we measure the alterations induced by block-cyclic sampling from the perspective of accuracy, fairness, and convergence rate. Experimental results indicate robustness to cycling over a two-block structure, e.g., due to time zones. In contrast, drawing data samples dependently from a multi-block structure significantly degrades the performance and rate of convergence by up to 26%. Moreover, we find that this performance degeneration is further aggravated by unbalanced block distributions to a point that can no longer be adequately compensated by higher communication and more frequent synchronization.
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
Department of Electrical and Computer Engineering, University of Windsor（威省大学电气与计算机工程系）；Faculty of Computer Science, University of New Brunswick（新不伦瑞克大学计算机科学学院）
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
Centre for Data Science, Queensland University of Technology（昆士兰科技大学数据科学中心）；School of Mathematical Sciences, Queensland University of Technology（昆士兰科技大学数学科学学院）；CEREMADE, Université Paris-Dauphine（巴黎第九大学CEREMADE研究中心）；Imperial College London（伦敦帝国学院）；Euler Institute, Università della Svizzera italiana (USI)（瑞士意大利语区大学欧拉研究所）；Insubria University（因苏布里亚大学）
Federated Causal Discovery from Heterogeneous Data
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）
Fairness-Constrained Optimization Attack in Federated Learning
University of Surrey, U.K.（英国萨里大学）；The Alan Turing Institute, London, U.K.（艾伦·图灵研究所）；The University of Warwick, U.K.（沃里克大学）；The University of Essex, UK（埃塞克斯大学）；Indian Institute of Technology (BHU), Varanasi（印度理工学院（班加罗尔））；University of Louisville, USA（路易斯维尔大学）；University of North Texas, USA（北德克萨斯大学）；Indian Institute of Technology Patna（印度理工学院帕纳布）
