SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G

TL;DR

SliceFed introduces a federated multi-agent deep reinforcement learning framework for dynamic spectrum slicing in 6G, effectively balancing spectral efficiency, interference constraints, and URLLC latency requirements in dense networks.

Contribution

It presents a novel federated constrained multi-agent DRL approach using a CMDP formulation with a primal-dual method and PPO, enabling privacy-preserving, reliable spectrum management in 6G networks.

Findings

Achieves nearly 100% URLLC latency satisfaction

Demonstrates robustness to traffic load variations

Converges to stable, safety-aware policies

Abstract

Dynamic spectrum slicing is a critical enabler for 6G Radio Access Networks (RANs), allowing the coexistence of heterogeneous services. However, optimizing resource allocation in dense, interference-limited deployments remains challenging due to non-stationary channel dynamics, strict Quality-of-Service (QoS) requirements, and the need for data privacy. In this paper, we propose SliceFed, a novel Federated Constrained Multi-Agent Deep Reinforcement Learning (F-MADRL) framework. SliceFed formulates the slicing problem as a Constrained Markov Decision Process (CMDP) where autonomous gNB agents maximize spectral efficiency while explicitly satisfying inter-cell interference budgets and hard ultra-reliable low-latency communication (URLLC) latency deadlines. We employ a Lagrangian primal-dual approach integrated with Proximal Policy Optimization (PPO) to enforce constraints, while Federated Averaging enables collaborative learning without exchanging raw local data. Extensive simulations in a dense multi-cell environment demonstrate that SliceFed converges to a stable, safety-aware policy. Unlike heuristic and unconstrained baselines, SliceFed achieves nearly 100% satisfaction of 1~ms URLLC latency deadlines and exhibits superior robustness to traffic load variations, verifying its potential for reliable and scalable 6G spectrum management.