FedKDD/FedMAS 2026: The 2026 International Joint Workshop on Federated Learning for Multi-agent Systems and Data Mining

Abstract

Multi-agent systems (MAS) are enabling increasingly complex, collaborative applications in autonomous driving, smart logistics, robotic coordination, and distributed sensing. Their effectiveness depends on collective intelligence emerging from multiple distributed agents, each operating with partial information and often sensitive local data. To realize such collaboration while preserving data privacy and autonomy, federated learning (FL) has emerged as a de facto decentralized framework that allows agents to learn shared models without centralizing raw data. Despite the rapid progress in both FL and MAS, significant challenges remain in integrating these paradigms—such as coordinating heterogeneous agents, handling non-IID data, ensuring communication efficiency, and maintaining system robustness and fairness in open environments. This workshop seeks to bring together researchers and practitioners from academia and industry to explore the convergence of federated learning and multi-agent systems. We aim to foster discussions on foundational advances, real-world deployments, and emerging interdisciplinary opportunities, with a focus on scalability, trustworthiness, adaptive coordination, and the broader societal impact of federated multi-agent intelligence.