DFedDG2: Distribution-Guided Gossip-Based Generalizable and Communication-Efficient Decentralized Federated Learning

Traditional Federated Learning (FL) focuses on collaborative global model training while ensuring privacy and personalization. Decentralized Federated Learning (DFL), a variant of FL, allows clients to independently manage and optimize local models without a central server. DFL reduces centralized communication bottlenecks and vulnerability to server failures or attacks. However, because the optimization dynamics change and there is no global model, generalization can suffer, making effective learning under data and model heterogeneity a critical challenge in DFL. Despite growing interest in DFL, the lack of distributional and uncertainty modeling in the literature limits reliability and effective generalization in non-IID settings. In this work, we propose DFedDG2, a personalized federated learning framework that operates within a peer-to-peer protocol. DFedDG2 offers the technical advantage of modeling each client’s local data distribution and exchanging this information with one-hop neighbors. In the decentralized network, clients perform distribution-aware gossip, where statistically similar clients exert greater influence to drive global alignment. This likelihood-weighted mixing fuses only a handful of vectors and scalars, significantly reducing communication costs while aligning semantic spaces across the network and enabling personalized training at the edge. In addition, theoretically, we prove that DFedDG2 achieves a sublinear convergence rate while the consensus error decays at a geometric rate under well-principled properties of gossip. Unlike the label-only non-IID experiments in DFL literature, we conduct extensive experiments on multiple data non-IID scenarios, topology variations, model heterogeneity, and uncertainty quantification, demonstrating the practical advantages of DFedDG2. Our results show that DFedDG2 not only achieves communication efficiency but also provides better generalization and improved reliability compared to state-of-the-art approaches.