arXiv:2607. 23987v1 Announce Type: cross Abstract: We study streaming federated learning with limited client memory, where newly generated training data incur time-varying sampling costs and must be selectively admitted and retained over time.
By Zhuoyi Zhao, Ben Liang
arXiv:2601. 19788v2 Announce Type: replace Abstract: Federated Continual Learning (FCL) leverages inter-client collaboration to better balance new knowledge acquisition and old knowledge retention on non-stationary data.
By Sixing Tan, Xianmin Liu
arXiv:2606. 08452v1 Announce Type: new Abstract: In many real-world settings, data streams are nonstationary and arrive sequentially, requiring learning systems to adapt continuously without retraining from scratch.
By Nazreen Shah, Govinda Arya, Bharath B. N., Ranjitha Prasad
The paper introduces FedSWE, a federated learning algorithm designed to handle non‑stationary and heterogeneous client availability without requiring prior real‑time knowledge of which devices are online. FedSWE compensates for missed computations, stabilizes global updates, and mixes local updates through implicit gossiping, all while adding only modest memory and computational overhead. The authors prove that FedSWE converges to a stationary point for non‑convex objectives and achieves linear speedup in certain scenarios, and they validate these claims with experiments on real‑world datasets featuring diverse client unavailability patterns.
By Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su
arXiv:2608. 07157v1 Announce Type: new Abstract: Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by device resources alone.
By Alireza Moayedikia, Alicia Troncoso Lora
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).
By Ferdous Pervej, Minseok Choi, Andreas F. Molisch