Decentralised federated learning (DFL) trains personalised models on devices that average with their neighbours rather than with a central server. Real devices, however, are often offline for hours or...
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.
arXiv:2606. 10774v2 Announce Type: replace Abstract: Decentralized Federated Learning(DFL) enables collaborative model training across wireless edge nodes, including IoT deployments, autonomous vehicles, UAV swarms, and satellite constellations.
arXiv:2607. 06979v1 Announce Type: new Abstract: Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refresh cycles due to the complexity of coordinating massive client populations.
arXiv:2609.23845v1 Announce Type: new Abstract: Decentralised federated learning removes the aggregation server but makes collaboration dependent on transient peer availability. In mobile and intermi...
arXiv:2607. 03171v1 Announce Type: cross Abstract: Decentralised federated learning, based on peer-to-peer communication, is increasingly proposed for on-device training of machine learning models, promising a privacy-preserving, communication-efficient training process with no risk of single-point failure.
arXiv:2608. 06946v1 Announce Type: cross Abstract: In gossip learning, a network of nodes trains a shared model collaboratively, without a central coordinator, by repeatedly exchanging parts of their local models.
The paper introduces SWB-DM, a Byzantine‑robust federated learning aggregator that treats each slice of a client update as a one‑dimensional distribution, computes a trimmed Wasserstein barycenter across clients, and uses a medoid‑based gauge‑fixing step to recover coordinate identity. It further incorporates delayed‑momentum caching to decouple robustness from the specific clients sampled each round. Extensive experiments on CIFAR‑10, CIFAR‑100, FEMNIST, and a 500‑client scalability run reveal distinct failure modes of existing defenses and demonstrate that SWB‑DM achieves significant gains, especially when compared under equal round budgets.
arXiv:2607. 08651v1 Announce Type: new Abstract: Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants.
arXiv:2609. 01952v1 Announce Type: cross Abstract: Decentralized, serverless learning increasingly connects devices running different architectures, where the standard tool, decentralized SGD, is undefined as models with different parameter counts cannot be averaged.
arXiv:2607. 28829v1 Announce Type: cross Abstract: Self-improving federated agent networks keep training after deployment by collecting new trajectories with the current policy and feeding them back into later rounds.
arXiv:2606. 25115v1 Announce Type: new Abstract: On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights.