A Network-Based Decentralization Scheme for Recommender Systems

Oregon Convention Center 

Recommender systems have witnessed significant advancements in the past decade, impacting billions of people worldwide. However, these systems often collect a vast amounts of personal data, raising concerns about privacy. To address these issues, federated methods have emerged, allowing models to be trained without sharing users' personal data with a central server. Despite these advancements, existing federated methods encounter challenges related to centralized bottlenecks and model aggregation between users. In this study, we present a fully decentralized federated learning approach, wherein each user's model is optimized using their own data and gradients transferred from their neighboring models. This ensures that personal data remains distributed and eliminates the necessity for central server-side aggregation or model merging steps. Empirical experiments demonstrate that our approach achieves a significant improvement in accuracy compared to other decentralized methods, across various network structures.