Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning

• URL: http://arxiv.org/abs/2511.19966v1
• Date: Tue, 25 Nov 2025 06:25:25 GMT
• Title: Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning
• Authors: Yujia Wang, Fenglong Ma, Jinghui Chen,
• Abstract summary: Asynchronous federated learning (FL) has recently gained attention for its enhanced efficiency and scalability.<n>We propose FedEcho, a novel framework that incorporates uncertainty-aware distillation to enhance the asynchronous FL performances.<n>We demonstrate that FedEcho consistently outperforms existing asynchronous federated learning baselines.
• Score: 61.249748418757946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Asynchronous federated learning (FL) has recently gained attention for its enhanced efficiency and scalability, enabling local clients to send model updates to the server at their own pace without waiting for slower participants. However, such a design encounters significant challenges, such as the risk of outdated updates from straggler clients degrading the overall model performance and the potential bias introduced by faster clients dominating the learning process, especially under heterogeneous data distributions. Existing methods typically address only one of these issues, creating a conflict where mitigating the impact of outdated updates can exacerbate the bias created by faster clients, and vice versa. To address these challenges, we propose FedEcho, a novel framework that incorporates uncertainty-aware distillation to enhance the asynchronous FL performances under large asynchronous delays and data heterogeneity. Specifically, uncertainty-aware distillation enables the server to assess the reliability of predictions made by straggler clients, dynamically adjusting the influence of these predictions based on their estimated uncertainty. By prioritizing more certain predictions while still leveraging the diverse information from all clients, FedEcho effectively mitigates the negative impacts of outdated updates and data heterogeneity. Through extensive experiments, we demonstrate that FedEcho consistently outperforms existing asynchronous federated learning baselines, achieving robust performance without requiring access to private client data.

Related papers

• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning [1.1510009152620668]
  asynchronous learning (FL) enables collaborative model training across distributed clients while preserving data locality.<n>We introduce Fast-and-Reliable AI Network, FRAIN, a new FL method that mitigates these limitations by incorporating two key ideas.<n>Experiments with a CNN image-classification model and a Transformer-based language model demonstrate that FRAIN achieves more stable and robust convergence than FedAvg, FedAsync, and BRAIN.
  arXiv  Detail & Related papers  (2025-05-07T08:20:23Z)
• Formal Logic-guided Robust Federated Learning against Poisoning Attacks [6.997975378492098]
  Federated Learning (FL) offers a promising solution to the privacy concerns associated with centralized Machine Learning (ML) FL is vulnerable to various security threats, including poisoning attacks, where adversarial clients manipulate the training data or model updates to degrade overall model performance. We present a defense mechanism designed to mitigate poisoning attacks in federated learning for time-series tasks.
  arXiv  Detail & Related papers  (2024-11-05T16:23:19Z)
• FedStale: leveraging stale client updates in federated learning [10.850101961203748]
  Federated learning algorithms are negatively affected by data heterogeneity and partial client participation. This paper shows that, when some clients participate much less than others, aggregating updates with different levels of staleness can detrimentally affect the training process. We introduce FedStale, a novel algorithm that updates the global model in each round through a convex combination of "fresh" updates from participating clients and "stale" updates from non-participating ones.
  arXiv  Detail & Related papers  (2024-05-07T10:11:42Z)
• 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)
• FLASH: Federated Learning Across Simultaneous Heterogeneities [55.0981921695672]
  FLASH(Federated Learning Across Simultaneous Heterogeneities) is a lightweight and flexible client selection algorithm.<n>It outperforms state-of-the-art FL frameworks under extensive sources of Heterogeneities.<n>It achieves substantial and consistent improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-13T20:04:39Z)
• Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients [21.104752782245257]
  Wireless Federated Learning (FL) is an emerging distributed machine learning paradigm. This paper proposes a novel risk-aware accelerated FL framework that accounts for the clients heterogeneity in the amount of possessed data. The proposed scheme is benchmarked against a conservative scheme (i.e., only allowing trustworthy devices) and an aggressive scheme (i.e. oblivious to the trust metric)
  arXiv  Detail & Related papers  (2024-01-17T15:15:52Z)
• Momentum Benefits Non-IID Federated Learning Simply and Provably [22.800862422479913]
  Federated learning is a powerful paradigm for large-scale machine learning. FedAvg and SCAFFOLD are two prominent algorithms to address these challenges. This paper explores the utilization of momentum to enhance the performance of FedAvg and SCAFFOLD.
  arXiv  Detail & Related papers  (2023-06-28T18:52:27Z)
• FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation [95.85026305874824]
  We introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiency.
  arXiv  Detail & Related papers  (2022-12-14T13:57:01Z)
• Towards Fair Federated Learning with Zero-Shot Data Augmentation [123.37082242750866]
  Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data. We propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server).
  arXiv  Detail & Related papers  (2021-04-27T18:23:54Z)

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.