FADAS: Towards Federated Adaptive Asynchronous Optimization

• URL: http://arxiv.org/abs/2407.18365v1
• Date: Thu, 25 Jul 2024 20:02:57 GMT
• Title: FADAS: Towards Federated Adaptive Asynchronous Optimization
• Authors: Yujia Wang, Shiqiang Wang, Songtao Lu, Jinghui Chen,
• Abstract summary: Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. This paper introduces federated adaptive asynchronous optimization, named FADAS, a novel method that incorporates asynchronous updates into adaptive federated optimization with provable guarantees. We rigorously establish the convergence rate of the proposed algorithms and empirical results demonstrate the superior performance of FADAS over other asynchronous FL baselines.
• Score: 56.09666452175333
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Federated learning (FL) has emerged as a widely adopted training paradigm for privacy-preserving machine learning. While the SGD-based FL algorithms have demonstrated considerable success in the past, there is a growing trend towards adopting adaptive federated optimization methods, particularly for training large-scale models. However, the conventional synchronous aggregation design poses a significant challenge to the practical deployment of those adaptive federated optimization methods, particularly in the presence of straggler clients. To fill this research gap, this paper introduces federated adaptive asynchronous optimization, named FADAS, a novel method that incorporates asynchronous updates into adaptive federated optimization with provable guarantees. To further enhance the efficiency and resilience of our proposed method in scenarios with significant asynchronous delays, we also extend FADAS with a delay-adaptive learning adjustment strategy. We rigorously establish the convergence rate of the proposed algorithms and empirical results demonstrate the superior performance of FADAS over other asynchronous FL baselines.

Related papers

• Efficient and Robust Regularized Federated Recommendation [52.24782464815489]
  The recommender system (RSRS) addresses both user preference and privacy concerns. We propose a novel method that incorporates non-uniform gradient descent to improve communication efficiency. RFRecF's superior robustness compared to diverse baselines.
  arXiv  Detail & Related papers  (2024-11-03T12:10:20Z)
• Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning [67.95280175998792]
  A novel adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association ins. We employ inverse RL (IRL) to automatically learn reward functions without manual tuning. We show that the proposed MA-AL method outperforms traditional RL approaches, achieving a $14.6%$ improvement in convergence and reward value.
  arXiv  Detail & Related papers  (2024-09-27T13:05:02Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Preconditioned Federated Learning [7.7269332266153326]
  Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. FedAvg has been considered to lack algorithm adaptivity compared to modern first-order adaptive optimizations. We propose new communication-efficient FL algortithms based on two adaptive frameworks: local adaptivity (PreFed) and server-side adaptivity (PreFedOp)
  arXiv  Detail & Related papers  (2023-09-20T14:58:47Z)
• 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)
• Accelerated Federated Learning with Decoupled Adaptive Optimization [53.230515878096426]
  federated learning (FL) framework enables clients to collaboratively learn a shared model while keeping privacy of training data on clients. Recently, many iterations efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings. This work aims to develop novel adaptive optimization methods for FL from the perspective of dynamics of ordinary differential equations (ODEs)
  arXiv  Detail & Related papers  (2022-07-14T22:46:43Z)
• Blockchain-enabled Server-less Federated Learning [5.065631761462706]
  We focus on an asynchronous server-less Federated Learning solution empowered by (BC) technology. In contrast to mostly adopted FL approaches, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates.
  arXiv  Detail & Related papers  (2021-12-15T07:41:23Z)
• Accelerating Federated Learning with a Global Biased Optimiser [16.69005478209394]
  Federated Learning (FL) is a recent development in the field of machine learning that collaboratively trains models without the training data leaving client devices. We propose a novel, generalised approach for applying adaptive optimisation techniques to FL with the Federated Global Biased Optimiser (FedGBO) algorithm. FedGBO accelerates FL by applying a set of global biased optimiser values during the local training phase of FL, which helps to reduce client-drift' from non-IID data.
  arXiv  Detail & Related papers  (2021-08-20T12:08:44Z)
• Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients [21.63719641718363]
  Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices. This paper presents a novel FL algorithm, namely Hybrid Federated Learning (HFL), to achieve a learning balance in efficiency and effectiveness.
  arXiv  Detail & Related papers  (2021-02-12T02:27:44Z)

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.