arXiv Machine Learning August 5, 2026 By Amin Farajzadeh, Melike Erol-Kantarci

FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs

Read the original on arXiv Machine Learning →

arXiv:2608. 03852v1 Announce Type: new Abstract: This paper proposes FedCritic-MIMO, a communication-efficient serverless federated multi-agent reinforcement learning framework for AI-native resource control across independently deployable cell-level controllers in open and disaggregated 6G RANs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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