mlsanityData to 14 Oct 2026

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

arXiv 2605.21418 ↗First posted 20 May 2026cs.LG cs.AI cs.CVcited by 1

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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.

Key facts. Cited by 1 paper 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

6 of 18 references.

References · 6

  1. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
  2. 2022Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworkscited by 9
  3. 2021Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networkscited by 6
  4. 2019S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimizationcited by 4
  5. 2022Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Controlcited by 3
  6. 20256G Cellular Networks: Mapping the Landscape for the IMT-2030 Frameworkcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANscited by 0

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Close in wording, no citation either way

  1. 2026SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G ↗
  2. 2024Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning
  3. 2025Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
  4. 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning
  5. 2025A Federated Deep Learning Framework for Cell-Free RSMA Networks ↗
  6. 2026Agentic AI as a Network Control-Plane Intelligence Layer for Federated Learning over 6G
  7. 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks
  8. 2024Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization