# FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G

arXiv: https://arxiv.org/abs/2605.21418 · HTML: https://mlsanity.com/paper/2605.21418/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 6 of 6)

- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks](https://mlsanity.com/paper/2205.05036/) (2022)
- [Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks](https://mlsanity.com/paper/2112.03465/) (2021)
- [S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization](https://mlsanity.com/paper/1910.08704/) (2019)
- [Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control](https://mlsanity.com/paper/2211.02296/) (2022)
- [6G Cellular Networks: Mapping the Landscape for the IMT-2030 Framework](https://mlsanity.com/paper/2501.14552/) (2025)

## Built on by (most cited first, 1 of 1)

- [FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs](https://mlsanity.com/paper/2608.03852/) (2026)

## Abstract (arXiv)

In sixth-generation (6G) ultra-dense networks, aggressive frequency reuse amplifies inter-cell interference (ICI), making multi-cell orthogonal frequency-division multiple access (OFDMA) scheduling and power control strongly coupled across neighboring cells. We study distributed downlink resource management -- joint subcarrier scheduling and power allocation -- under interference coupling and long-term per-user quality-of-service (QoS) minimum-rate constraints. By using virtual-queue deficit weights to enforce long-term QoS, we develop FedCritic, a serverless federated multi-agent actor-critic framework with decentralized execution. Unlike centralized training with decentralized execution (CTDE) approaches that require centralized critic learning and joint trajectory aggregation, FedCritic federates the critic through lightweight gossip-based parameter averaging over the interference graph, enabling stable value estimation without a central coordinator while keeping policies local. Simulations in an interference-rich reuse-1 setting show that FedCritic improves mean signal-to-interference-plus-noise ratio (SINR) and cell-edge rate, increases network-wide average sum-rate and fairness relative to non-coordinated and CTDE baselines, and achieves more stable training with lower coordination overhead.