Aug 2026· 2026 International Workshop on Intelligent Systems (IWIS)· pp. 1-6· 0 citations· 11 references
Abstract
Asynchronous Federated Learning (AFL) addresses the synchronization bottleneck of traditional federated learning by allowing clients to upload local model updates independently. However, asynchronous communication can result in stale updates, whose optimization information can become outdated in cases of heterogeneous (Non-IID) data distribution. Momentumbased approaches typically have a fixed momentum coefficient, which implies that fresh and stale updates are both reliable. We propose Context-Aware Dynamic Momentum (CADM), a staleness-aware momentum adaptation system that dynamically adjusts the momentum coefficient based on update staleness using an exponential decay function. Preliminary were conducted under multiple combinations of data heterogeneity and network conditions, including momentum decay strategy evaluation and long-term training analysis. Experimental results demonstrate that CADM consistently outperforms the conventional fixedmomentum approach, achieving up to an 18.7 percentage-point improvement under severe Non-IID conditions while reducing the regression gap from 16.4 to 4.9 percentage points. These results indicate that adaptive momentum based on update staleness effectively improves optimization consistency and robustness in heterogeneous asynchronous federated learning environments.
Asynchronous federated learning is a distributed machine learning framework designed for parallel and distributed systems. However, system heterogeneity in communication and computation can cause stragglers to upload stale updates, which may degrade the performance of the global model. Existing studies primarily focus...
Zhen-Hao Liu, Meng-Yao Li, Bo Zhang et al.· IEEE Transactions on Paralle...· 0 citations
An asynchronous framework named FedQS, which employs a multi-dimensional staleness evaluation mechanism that dynamically assesses updates by combining the similarity between local and global models with client latency metrics, and implements a decoupling solution via a queue scheduling algorithm to resolve the coupling...
Jia-Hui Zhou, Fang Li, Tian-Yu Shi et al.· Journal of Cloud Computing· 0 citations
OrthoFL is proposed, an orthogonal calibration framework that decouples global and local learning progress to reduce interference and consistently outperforms state-of-the-art asynchronous baselines under various delay patterns and heterogeneity scenarios.
Jiayun Zhang, Shuheng Li, Haiyu Huang et al.· Proceedings of the 32nd ACM...· 0 citations
Heterogeneous Split Federated Learning (HSFL) is a privacy-preserving technique for devices in different edge settings. However, the present methods adopt a static model partition that cannot adapt to network conditions, resulting in longer inference latency and higher communication costs. To address this issue, we pro...
M. Farooq, Paolo Bellavista, Hafiza Rohma Ahmed· 2026 International Conferenc...· 0 citations
Split Federated Learning (SFL) has emerged as a pivotal paradigm for privacy-preserving distributed training on resource-constrained edge devices by partitioning neural networks between clients and a server. A critical design choice in SFL is the split layer, which determines the computation distribution and the semant...
Ai-Jing Li, Ya-Wen Li, Guan-Hua Ye et al.· Proceedings of the Thirty-Fi...· 0 citations
Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather than waiting for all selected clients to finish. While efficient, this arrival-driven aggregation can silently distort representation learning under heterogeneous particip...
Prashant Bajpai, Divya Saxena, Philippe Lalanda et al.· 0 citations
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