Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

• URL: http://arxiv.org/abs/2112.14332v4
• Date: Wed, 31 May 2023 20:45:25 GMT
• Title: Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
• Authors: Boxin Zhao, Lingxiao Wang, Mladen Kolar, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, and Chaochao Chen
• Abstract summary: federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. Despite its importance, there is limited work on how to sample clients effectively. We show how our sampling method can improve the convergence speed of optimization algorithms.
• Score: 36.05851452151107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models. Despite its importance, there is limited work on how to sample clients effectively. In this paper, we cast client sampling as an online learning task with bandit feedback, which we solve with an online stochastic mirror descent (OSMD) algorithm designed to minimize the sampling variance. We then theoretically show how our sampling method can improve the convergence speed of optimization algorithms. To handle the tuning parameters in OSMD that depend on the unknown problem parameters, we use the online ensemble method and doubling trick. We prove a dynamic regret bound relative to any sampling sequence. The regret bound depends on the total variation of the comparator sequence, which naturally captures the intrinsic difficulty of the problem. To the best of our knowledge, these theoretical contributions are new and the proof technique is of independent interest. Through both synthetic and real data experiments, we illustrate advantages of the proposed client sampling algorithm over the widely used uniform sampling and existing online learning based sampling strategies. The proposed adaptive sampling procedure is applicable beyond the FL problem studied here and can be used to improve the performance of stochastic optimization procedures such as stochastic gradient descent and stochastic coordinate descent.

Related papers

• Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling [15.027267764009052]
  Federated Learning (FL) algorithms sample a random subset of clients to address the straggler issue and improve communication efficiency. Recent have proposed various client sampling methods, but they have limitations in joint system and data heterogeneity. We propose a new independent client sampling strategy to minimize the wall-clock time of FL.
  arXiv  Detail & Related papers  (2024-02-15T16:51:38Z)
• Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance [37.646655530394604]
  Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. We present the first adaptive client sampler, K-Vib, employing an independent sampling procedure. K-Vib achieves a linear speed-up on the regret bound $tildemathcalObig(Nfrac13Tfrac23/Kfrac43big)$ within a set communication budget.
  arXiv  Detail & Related papers  (2023-10-04T10:08:01Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• Learning Sampling Distributions for Model Predictive Control [36.82905770866734]
  Sampling-based approaches to Model Predictive Control (MPC) have become a cornerstone of contemporary approaches to MPC. We propose to carry out all operations in the latent space, allowing us to take full advantage of the learned distribution. Specifically, we frame the learning problem as bi-level optimization and show how to train the controller with backpropagation-through-time.
  arXiv  Detail & Related papers  (2022-12-05T20:35:36Z)
• Faster Adaptive Federated Learning [84.38913517122619]
  Federated learning has attracted increasing attention with the emergence of distributed data. In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on momentum-based variance reduced technique in cross-silo FL.
  arXiv  Detail & Related papers  (2022-12-02T05:07:50Z)
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning [57.163525407022966]
  Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. We propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions.
  arXiv  Detail & Related papers  (2022-08-26T04:28:01Z)
• Sampling Through the Lens of Sequential Decision Making [9.101505546901999]
  We propose a reward-guided sampling strategy called Adaptive Sample with Reward (ASR) Our approach optimally adjusts the sampling process to achieve optimal performance. Empirical results in information retrieval and clustering demonstrate ASR's superb performance across different datasets.
  arXiv  Detail & Related papers  (2022-08-17T04:01:29Z)
• Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling [34.187387951367526]
  Federated learning (FL) algorithms usually sample a fraction in each (partial participation) when the number of participants is large. Recent works have focused on the convergence analysis of FL. We obtain new convergence bound for FL algorithms with arbitrary client sampling probabilities.
  arXiv  Detail & Related papers  (2021-12-21T14:28:40Z)
• Local policy search with Bayesian optimization [73.0364959221845]
  Reinforcement learning aims to find an optimal policy by interaction with an environment. Policy gradients for local search are often obtained from random perturbations. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
  arXiv  Detail & Related papers  (2021-06-22T16:07:02Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity [57.275753974812666]
  Federated learning involves learning from data samples distributed across a network of clients while the data remains local. In this paper, we propose a novel straggler-resilient federated learning method that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure.
  arXiv  Detail & Related papers  (2020-12-28T19:21:14Z)

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.