FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning

• URL: http://arxiv.org/abs/2108.06869v5
• Date: Sun, 16 Apr 2023 16:53:18 GMT
• Title: FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning
• Authors: Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti, Sewoong Oh
• Abstract summary: Federated learning (FL) aims to minimize the communication complexity of training a model over heterogeneous data distributed across many clients. We propose FedChain, an algorithmic framework that combines the strengths of local methods and global methods to achieve fast convergence in terms of R.
• Score: 24.812767482563878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) aims to minimize the communication complexity of training a model over heterogeneous data distributed across many clients. A common approach is local methods, where clients take multiple optimization steps over local data before communicating with the server (e.g., FedAvg). Local methods can exploit similarity between clients' data. However, in existing analyses, this comes at the cost of slow convergence in terms of the dependence on the number of communication rounds R. On the other hand, global methods, where clients simply return a gradient vector in each round (e.g., SGD), converge faster in terms of R but fail to exploit the similarity between clients even when clients are homogeneous. We propose FedChain, an algorithmic framework that combines the strengths of local methods and global methods to achieve fast convergence in terms of R while leveraging the similarity between clients. Using FedChain, we instantiate algorithms that improve upon previously known rates in the general convex and PL settings, and are near-optimal (via an algorithm-independent lower bound that we show) for problems that satisfy strong convexity. Empirical results support this theoretical gain over existing methods.

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)
• 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)
• Composite federated learning with heterogeneous data [11.40641907024708]
  We propose a novel algorithm for solving the composite Federated Learning (FL) problem. This algorithm manages non-smooth regularization by strategically decoupling the proximal operator and communication, and addresses client drift without any assumptions about data similarity. We prove that our algorithm converges linearly to a neighborhood of the optimal solution and demonstrate the superiority of our algorithm over state-of-the-art methods in numerical experiments.
  arXiv  Detail & Related papers  (2023-09-04T20:22:57Z)
• 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)
• Locally Adaptive Federated Learning [30.19411641685853]
  Federated learning is a paradigm of distributed machine learning in which multiple clients coordinate with a central server to learn a model. Standard federated optimization methods such as Federated Averaging (FedAvg) ensure generalization among the clients. We propose locally federated learning algorithms, that leverage the local geometric information for each client function.
  arXiv  Detail & Related papers  (2023-07-12T17:02:32Z)
• Federated Minimax Optimization with Client Heterogeneity [11.558008138030845]
  Minimax computation has seen a surge in interest with the advent modern applications such as GANs. We propose a general federated minimax framework that subsumes settings and existing methods like Local SGDA.
  arXiv  Detail & Related papers  (2023-02-08T18:33:55Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• 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)
• FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data [59.50904660420082]
  Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model.
  arXiv  Detail & Related papers  (2020-05-22T23:07:42Z)

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.