Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1109/TSMC.2025.3648504 |
| Title (Primary) | Asynchronous federated learning: A scalable approach for decentralized machine learning |
| Author | Forootani, A.; Iervolino, R. |
| Source Titel | IEEE Transactions on Systems Man Cybernetics-Systems |
| Year | 2026 |
| Department | SANA |
| Volume | 56 |
| Issue | 3 |
| Page From | 2062 |
| Page To | 2075 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Keywords | Asynchronous federated learning (AFL); client drifts; deep neural networks; federated learning (FL); stochastic gradient descent (SGD); |
| Abstract | Federated Learning (FL) has emerged as a powerful paradigm for
decentralized machine learning, enabling collaborative model training
across diverse clients without sharing raw data. However, traditional FL
approaches often face limitations in scalability and efficiency due to
their reliance on synchronous client updates, which can result in
significant delays and increased communication overhead, particularly in
heterogeneous and dynamic environments. To address these challenges in
this paper, we propose an Asynchronous Federated Learning (AFL)
algorithm, which allows clients to update the global model independently
and asynchronously. Our key contributions include a comprehensive
convergence analysis of AFL in the presence of client delays and model
staleness. By leveraging martingale difference sequence theory and
variance bounds, we ensure robust convergence despite asynchronous
updates. Assuming strongly convex local objective functions, we
establish bounds on gradient variance under random client sampling and
derive a recursion formula quantifying the impact of client delays on
convergence.
The proposed AFL algorithm addresses key limitations of traditional FL methods, such as inefficiency due to global synchronization and susceptibility to client drift. It enhances scalability, robustness, and efficiency in real-world settings with heterogeneous client populations and dynamic network conditions. Our results underscore the potential of AFL to drive advancements in distributed learning systems, particularly for large-scale, privacy-preserving applications in resource-constrained environments |
| Forootani, A., Iervolino, R. (2026): Asynchronous federated learning: A scalable approach for decentralized machine learning IEEE Trans. Syst. Man Cybern. -Syst. 56 (3), 2062 - 2075 10.1109/TSMC.2025.3648504 |
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