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

arXiv: https://arxiv.org/abs/2608.03852 · HTML: https://mlsanity.com/paper/2608.03852/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Error Feedback Fixes SignSGD and other Gradient Compression Schemes](https://mlsanity.com/paper/1901.09847/) (2019)
- [Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication](https://mlsanity.com/paper/1902.00340/) (2019)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.03052/) (2024)
- [Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2002.06215/) (2020)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis](https://mlsanity.com/paper/2109.03699/) (2021)
- [Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing](https://mlsanity.com/paper/2208.01736/) (2022)
- [Empowering the 6G Cellular Architecture with Open RAN](https://mlsanity.com/paper/2312.02746/) (2023)
- [Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks](https://mlsanity.com/paper/2309.06021/) (2023)
- [Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G](https://mlsanity.com/paper/2502.05812/) (2025)
- [Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey](https://mlsanity.com/paper/2401.04934/) (2024)
- [Meta Federated Reinforcement Learning for Distributed Resource Allocation](https://mlsanity.com/paper/2307.02900/) (2023)
- [6G Cellular Networks: Mapping the Landscape for the IMT-2030 Framework](https://mlsanity.com/paper/2501.14552/) (2025)
- [Communication-Efficient Soft Actor-Critic Policy Collaboration via Regulated Segment Mixture](https://mlsanity.com/paper/2312.10123/) (2023)
- [Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Services](https://mlsanity.com/paper/2508.16037/) (2025)
- [FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G](https://mlsanity.com/paper/2605.21418/) (2026)

## Abstract (arXiv)

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. Controllers share no trainer, retain local actors and personalized critic components, and exchange only compatible shared critic parameters. FedCritic-MIMO targets reuse-1 multi-cell massive-MIMO OFDMA deployments, where RAN controllers jointly manage user scheduling, per-stream power allocation, beamforming, interference, and long-term QoS with limited inter-controller signaling. Each base station locally executes its actor without centralized training or actor federation, while critic knowledge is exchanged peer-to-peer over an interference-aware graph. It enables this collaboration through wireless-aware event triggering, adaptive layer-wise top-k sparse critic exchange with error feedback, and balanced interference-aware fusion. We establish conditional finite-time stationarity and consensus guarantees for the balanced, compressed peer-to-peer critic recursion under a fixed-policy, frozen-target critic-regression model. In strongly interference-coupled reuse-1 simulations, FedCritic-MIMO achieves the best performance-communication tradeoff among heuristic, independent-learning, centralized-training, and communication-ablation baselines. It achieves the highest held-out throughput, improves user-rate distribution and mean SINR, increases QoS satisfaction, and attains the lowest interference cost per delivered bit among learning baselines. It reduces critic-communication overhead by 76% relative to uncompressed distributed critic exchange. These results demonstrate that serverless exchange of compatible shared critic parameters can coordinate RAN controllers without centralized trajectory collection or parameter-server aggregation.