The paper introduces DMFL-SQ, a decentralized multi-task learning algorithm that integrates graph-based personalization, agnostic fairness, and compressed event-triggered communication. It provides convergence guarantees for non-convex objectives, achieving an ≠O(T^{-1/2}) stationarity rate despite sparse, quantized, and event-triggered communication, and offers PAC-Bayes generalization bounds for the fairness objective. Experiments on CIFAR-10 and the MUSMET EEG dataset show that DMFL-SQ reduces communication while preserving predictive performance and improving fairness across clients.
arXiv:2608. 15256v1 Announce Type: new Abstract: Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL).
arXiv:2508. 12042v3 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data.
arXiv:2603. 01730v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) has emerged as a transformative server-free paradigm that enables collaborative learning over large-scale heterogeneous networks.
The paper introduces RoCo-FL, a robust and convergent framework for Over-the-Air Federated Learning that addresses channel noise, fading, and data/system heterogeneity. RoCo-FL models local updates with inexact optimization and a proximal constraint to stabilize updates under wireless impairments and stragglers. The authors provide convergence guarantees for non-convex objectives and demonstrate improved stability and accuracy on CIFAR10, CIFAR100, and FEMNIST compared to existing baselines.
arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.
arXiv:2505. 09854v3 Announce Type: replace Abstract: As end-user device capability increases and demand for intelligent services at the Internet's edge rises, distributed learning has emerged as a key enabling technology for the intelligent edge.
Wireless Internet-of-Things (IoT) edge networks require decentralized learning (DecL) methods that can operate reliably under both heterogeneous local data and communication-constrained wireless links...
The paper introduces MANET-GNN, a message‑passing graph neural network designed to perform decentralized power allocation in dynamic, multi‑hop, multi‑channel mobile ad hoc networks (MANETs). It formulates a constrained throughput maximization problem that includes various traffic patterns such as unicast, multicast, and convergecast, and uses this as an unsupervised training objective. MANET‑GNN operates with only local, possibly noisy channel state information and a limited number of neighbor exchanges, achieving performance comparable to centralized solutions while scaling across different network topologies and sizes.
arXiv:2504. 21296v2 Announce Type: replace Abstract: Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques.
MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple frequency channels and support heterogeneous traffi...
Fed-GRPO is a federated training framework for Large Language Models that extends Group Relative Policy Optimization (GRPO) to operate without sharing raw data. It introduces three reward‑signal‑driven mechanisms—signal‑weighted aggregation, global reward calibration, and adaptive sparse communication—to guide client updates based on reward statistics. Experiments on mathematical reasoning tasks show that Fed‑GRPO outperforms other federated methods, approaches centralized performance, and reduces communication by up to 32× while allowing extreme compression with minimal accuracy loss.
arXiv:2607. 29441v1 Announce Type: new Abstract: Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages.