Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction

• URL: http://arxiv.org/abs/2310.08670v2
• Date: Thu, 26 Oct 2023 20:35:47 GMT
• Title: Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction
• Authors: Hanhan Zhou, Tian Lan, Guru Venkataramani and Wenbo Ding
• Abstract summary: Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks. Recent research efforts have focused on model-heterogeneous FL, by extracting reduced-size models from the global model and applying them to local clients accordingly. This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.
• Score: 22.567754688492414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks. Recent research efforts have focused on model-heterogeneous FL, by extracting reduced-size models from the global model and applying them to local clients accordingly. Despite the empirical success, general theoretical guarantees of convergence on this method remain an open question. This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time. In particular, we prove that under certain sufficient conditions and for both IID and non-IID data, these algorithms converge to a stationary point of standard FL for general smooth cost functions. Moreover, we introduce the concept of minimum coverage index, together with model reduction noise, which will determine the convergence of heterogeneous federated learning, and therefore we advocate for a holistic approach that considers both factors to enhance the efficiency of heterogeneous federated learning.

Related papers

• On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness [16.595935469099306]
  We propose FLAME, an optimization framework by utilizing the alternating direction method of multipliers (ADMM) to train personalized and global models. Our theoretical analysis establishes the global convergence and two kinds of convergence rates for FLAME under mild assumptions. Our experimental findings show that FLAME outperforms state-of-the-art methods in convergence and accuracy, and it achieves higher test accuracy under various attacks.
  arXiv  Detail & Related papers  (2024-07-23T11:35:42Z)
• Aggregation Weighting of Federated Learning via Generalization Bound Estimation [65.8630966842025]
  Federated Learning (FL) typically aggregates client model parameters using a weighting approach determined by sample proportions. We replace the aforementioned weighting method with a new strategy that considers the generalization bounds of each local model.
  arXiv  Detail & Related papers  (2023-11-10T08:50:28Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Deep Equilibrium Models Meet Federated Learning [71.57324258813675]
  This study explores the problem of Federated Learning (FL) by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning.
  arXiv  Detail & Related papers  (2023-05-29T22:51:40Z)
• Adaptive Federated Learning via New Entropy Approach [14.595709494370372]
  Federated Learning (FL) has emerged as a prominent distributed machine learning framework. In this paper, we propose an adaptive FEDerated learning algorithm based on ENTropy theory (FedEnt) to alleviate the parameter deviation among heterogeneous clients.
  arXiv  Detail & Related papers  (2023-03-27T07:57:04Z)
• 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)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)
• On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning [15.300983585090794]
  We present a unifying framework for heterogeneous FL algorithms with em arbitrary adaptive online model pruning. In particular, we prove that under certain sufficient conditions, these algorithms converge to a stationary point of standard FL for general smooth cost functions. We illuminate two key factors impacting convergence: pruning-induced noise and minimum coverage index.
  arXiv  Detail & Related papers  (2022-01-27T20:43:38Z)
• TsmoBN: Interventional Generalization for Unseen Clients in Federated Learning [23.519212374186232]
  We form a training structural causal model (SCM) to explain the challenges of model generalization in a distributed learning paradigm. We present a simple yet effective method using test-specific and momentum tracked batch normalization (TsmoBN) to generalize FL models to testing clients.
  arXiv  Detail & Related papers  (2021-10-19T13:46:37Z)
• Federated Ensemble Model-based Reinforcement Learning in Edge Computing [21.840086997141498]
  Federated learning (FL) is a privacy-preserving distributed machine learning paradigm. We propose a novel FRL algorithm that effectively incorporates model-based RL and ensemble knowledge distillation into FL for the first time. Specifically, we utilise FL and knowledge distillation to create an ensemble of dynamics models for clients, and then train the policy by solely using the ensemble model without interacting with the environment.
  arXiv  Detail & Related papers  (2021-09-12T16:19:10Z)

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.