AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation

• URL: http://arxiv.org/abs/2204.13170v4
• Date: Mon, 24 Jul 2023 13:35:28 GMT
• Title: AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
• Authors: Farshid Varno, Marzie Saghayi, Laya Rafiee Sevyeri, Sharut Gupta, Stan Matwin, Mohammad Havaei
• Abstract summary: In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data. In order to estimate and therefore remove this drift, variance reduction techniques have been incorporated into FL optimization recently. We propose an adaptive algorithm that accurately estimates drift across clients.
• Score: 12.62716075696359
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data. Models are optimized locally at each client and further communicated to a central hub for aggregation. While FL is an appealing decentralized training paradigm, heterogeneity among data from different clients can cause the local optimization to drift away from the global objective. In order to estimate and therefore remove this drift, variance reduction techniques have been incorporated into FL optimization recently. However, these approaches inaccurately estimate the clients' drift and ultimately fail to remove it properly. In this work, we propose an adaptive algorithm that accurately estimates drift across clients. In comparison to previous works, our approach necessitates less storage and communication bandwidth, as well as lower compute costs. Additionally, our proposed methodology induces stability by constraining the norm of estimates for client drift, making it more practical for large scale FL. Experimental findings demonstrate that the proposed algorithm converges significantly faster and achieves higher accuracy than the baselines across various FL benchmarks.

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)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• 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)
• Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning [9.975023463908496]
  Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. We propose a novel regularization technique based on adaptive self-distillation (ASD) for training models on the client side. Our regularization scheme adaptively adjusts to the client's training data based on the global model entropy and the client's label distribution.
  arXiv  Detail & Related papers  (2023-05-31T07:00:42Z)
• Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms [29.636944156801327]
  Multiple clients collaboratively train one global model without sharing their semantic parsing data. Lorar adjusts each client's contribution to the global model update based on its training loss reduction during each round. Clients with smaller datasets enjoy larger performance gains.
  arXiv  Detail & Related papers  (2023-05-26T19:25:49Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Federated Learning under Heterogeneous and Correlated Client Availability [10.05687757555923]
  This paper presents the first convergence analysis for a FedAvg-like FL algorithm under heterogeneous and correlated client availability. We propose CA-Fed, a new FL algorithm that tries to balance the conflicting goals of maximizing convergence speed and minimizing model bias. Our experimental results show that CA-Fed achieves higher time-average accuracy and a lower standard deviation than state-of-the-art AdaFed and F3AST.
  arXiv  Detail & Related papers  (2023-01-11T18:38:48Z)
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training [61.231021417674235]
  Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy. We propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage. Our experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep.
  arXiv  Detail & Related papers  (2022-03-05T02:31:32Z)
• Local Adaptivity in Federated Learning: Convergence and Consistency [25.293584783673413]
  Federated learning (FL) framework trains a machine learning model using decentralized data stored at edge client devices by periodically aggregating locally trained models. We show in both theory and practice that while local adaptive methods can accelerate convergence, they can cause a non-vanishing solution bias. We propose correction techniques to overcome this inconsistency and complement the local adaptive methods for FL.
  arXiv  Detail & Related papers  (2021-06-04T07:36:59Z)
• 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)
• 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)

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.