Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates

• URL: http://arxiv.org/abs/2402.12780v2
• Date: Mon, 10 Jun 2024 13:43:21 GMT
• Title: Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
• Authors: Youssef Allouah, Sadegh Farhadkhani, Rachid GuerraouI, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych,
• Abstract summary: adversarial (a.k.a., em Byzantine) clients makes federated learning (FL) prone to arbitrary manipulation. We show that the rate improvement in learning accuracy em diminishes with respect to the number of subsampled clients. We also observe that under a careful step choice, the learning error due to Byzantine clients decreases with the number of local steps.
• Score: 11.616782769625003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The possibility of adversarial (a.k.a., {\em Byzantine}) clients makes federated learning (FL) prone to arbitrary manipulation. The natural approach to robustify FL against adversarial clients is to replace the simple averaging operation at the server in the standard $\mathsf{FedAvg}$ algorithm by a \emph{robust averaging rule}. While a significant amount of work has been devoted to studying the convergence of federated {\em robust averaging} (which we denote by $\mathsf{FedRo}$), prior work has largely ignored the impact of {\em client subsampling} and {\em local steps}, two fundamental FL characteristics. While client subsampling increases the effective fraction of Byzantine clients, local steps increase the drift between the local updates computed by honest (i.e., non-Byzantine) clients. Consequently, a careless deployment of $\mathsf{FedRo}$ could yield poor performance. We validate this observation by presenting an in-depth analysis of $\mathsf{FedRo}$ tightly analyzing the impact of client subsampling and local steps. Specifically, we present a sufficient condition on client subsampling for nearly-optimal convergence of $\mathsf{FedRo}$ (for smooth non-convex loss). Also, we show that the rate of improvement in learning accuracy {\em diminishes} with respect to the number of clients subsampled, as soon as the sample size exceeds a threshold value. Interestingly, we also observe that under a careful choice of step-sizes, the learning error due to Byzantine clients decreases with the number of local steps. We validate our theory by experiments on the FEMNIST and CIFAR-$10$ image classification tasks.

Related papers

• Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays [0.0]
  Federated learning (FL) was recently proposed to securely train models with data held over multiple locations ("clients") Two major challenges hindering the performance of FL algorithms are long training times caused by straggling clients, and a decline in model accuracy under non-iid local data distributions ("client drift") We propose and analyze Asynchronous Exact Averaging (AREA), a new (sub)gradient algorithm that utilizes communication to speed up convergence and enhance scalability, and employs client memory to correct the client drift caused by variations in client update frequencies.
  arXiv  Detail & Related papers  (2024-05-16T14:22:49Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications [26.597515045714502]
  Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively train a model via a global objective. We show that when the channel conditions are heterogeneous across clients are changing over time, the FedFederated Postponed global model fails to postpone the gossip-type information mixing errors.
  arXiv  Detail & Related papers  (2023-06-01T01:52:03Z)
• Federated Learning with Regularized Client Participation [1.433758865948252]
  Federated Learning (FL) is a distributed machine learning approach where multiple clients work together to solve a machine learning task. One of the key challenges in FL is the issue of partial participation, which occurs when a large number of clients are involved in the training process. We propose a new technique and design a novel regularized client participation scheme.
  arXiv  Detail & Related papers  (2023-02-07T18:26:07Z)
• Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework [82.36466358313025]
  We propose a primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. Experiments based on (semi-supervised) image classification tasks demonstrate superiority of FedVRA over the existing schemes.
  arXiv  Detail & Related papers  (2022-12-03T03:27:51Z)
• Combating Client Dropout in Federated Learning via Friend Model Substitution [8.325089307976654]
  Federated learning (FL) is a new distributed machine learning framework known for its benefits on data privacy and communication efficiency. This paper studies a passive partial client participation scenario that is much less well understood. We develop a new algorithm FL-FDMS that discovers friends of clients whose data distributions are similar. Experiments on MNIST and CIFAR-10 confirmed the superior performance of FL-FDMS in handling client dropout in FL.
  arXiv  Detail & Related papers  (2022-05-26T08:34:28Z)
• Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization [6.935471115003109]
  We present a theoretical study of federated learning of client updates. In particular, we prove that whenever localizes are small, one can take an update over all clients. We also prove that the noise of client sampling can be controlled by using a small server-side stepsize.
  arXiv  Detail & Related papers  (2022-01-26T17:26:25Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation [119.19061102064497]
  We propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL) In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, competes to generate a block based on the received models, and then aggregates the models from the generated block before its local training of the next round. We explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
  arXiv  Detail & Related papers  (2021-01-18T07:19:08Z)
• Faster Non-Convex Federated Learning via Global and Local Momentum [57.52663209739171]
  textttFedGLOMO is the first (first-order) FLtexttFedGLOMO algorithm. Our algorithm is provably optimal even with communication between the clients and the server.
  arXiv  Detail & Related papers  (2020-12-07T21:05:31Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients [124.48732110742623]
  We propose a novel framework by integrating blockchain into Federated Learning (FL) BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning. It gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors.
  arXiv  Detail & Related papers  (2020-12-02T12:18:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.