Asynchronous Wireless Federated Learning with Probabilistic Client Selection

• URL: http://arxiv.org/abs/2311.16741v1
• Date: Tue, 28 Nov 2023 12:39:34 GMT
• Title: Asynchronous Wireless Federated Learning with Probabilistic Client Selection
• Authors: Jiarong Yang, Yuan Liu, Fangjiong Chen, Wen Chen, Changle Li
• Abstract summary: Federated learning (FL) is a promising distributed learning framework where clients collaboratively train a machine learning model coordinated by a server. We consider that each client keeps local updates and probabilistically transmits the local model. We develop an iterative algorithm to solve the non probabilistic convergence problem optimally globally.
• Score: 20.882840344104135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server. To tackle the stragglers issue in asynchronous FL, we consider that each client keeps local updates and probabilistically transmits the local model to the server at arbitrary times. We first derive the (approximate) expression for the convergence rate based on the probabilistic client selection. Then, an optimization problem is formulated to trade off the convergence rate of asynchronous FL and mobile energy consumption by joint probabilistic client selection and bandwidth allocation. We develop an iterative algorithm to solve the non-convex problem globally optimally. Experiments demonstrate the superiority of the proposed approach compared with the traditional schemes.

Related papers

• Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning [51.560590617691005]
  We investigate whether it is possible to squeeze more juice" out of each cohort than what is possible in a single communication round. Our approach leads to up to 74% reduction in the total communication cost needed to train a FL model in the cross-device setting.
  arXiv  Detail & Related papers  (2024-06-03T08:48:49Z)
• 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)
• Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients [30.135431295658343]
  Federated learning (FL) aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients. In this paper, we propose an efficient federated learning (AFL) framework called DeFedAvg. DeFedAvg is the first AFL algorithm that achieves the desirable linear speedup property, which indicates its high scalability.
  arXiv  Detail & Related papers  (2024-02-17T05:22:46Z)
• 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)
• Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach [16.093068118849246]
  This paper presents a novel split federated learning (SFL) framework that pairs clients with different computational resources. A greedy algorithm is proposed by reconstructing the optimization of training latency as a graph edge selection problem. Simulation results show the proposed method can significantly improve the FL training speed and achieve high performance.
  arXiv  Detail & Related papers  (2023-08-26T11:10:54Z)
• 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 Control of Client Selection and Gradient Compression for Efficient Federated Learning [28.185096784982544]
  Federated learning (FL) allows multiple clients cooperatively train models without disclosing local data. We propose a heterogeneous-aware FL framework, called FedCG, with adaptive client selection and gradient compression. Experiments on both real-world prototypes and simulations show that FedCG can provide up to 5.3$times$ speedup compared to other methods.
  arXiv  Detail & Related papers  (2022-12-19T14:19:07Z)
• FL Games: A Federated Learning Framework for Distribution Shifts [71.98708418753786]
  Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. We propose FL GAMES, a game-theoretic framework for federated learning that learns causal features that are invariant across clients.
  arXiv  Detail & Related papers  (2022-10-31T22:59:03Z)
• Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks [2.6774008509840996]
  Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to obtain reliable specialized models. This paper proposes a new client selection algorithm that aims to accelerate the convergence rate for obtaining specialized machine learning models.
  arXiv  Detail & Related papers  (2021-08-16T21:38:22Z)
• 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.