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cs.LG2025
Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
Haoran Zhang, Zejun Gong, Zekai Li +3
Federated learning (FL) allows edge devices to collaboratively train models without sharing local data. As FL gains popularity, clients may need to train multiple unrelated FL mode…
cs.AI2025
LLM-Powered Decentralized Generative Agents with Adaptive Hierarchical Knowledge Graph for Cooperative Planning
Hanqing Yang, Jingdi Chen, Marie Siew +2
Developing intelligent agents for long-term cooperation in dynamic open-world scenarios is a major challenge in multi-agent systems. Traditional Multi-agent Reinforcement Learning…