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.

翻译：动态频谱切片是实现6G无线接入网（RAN）中异构服务共存的关键使能技术。然而，在密集且干扰受限的部署场景中，由于非平稳的信道动态特性、严格的服务质量（QoS）要求以及数据隐私保护需求，资源分配优化仍面临挑战。本文提出SliceFed，一种新颖的联邦约束多智能体深度强化学习（F-MADRL）框架。SliceFed将频谱切片问题建模为约束马尔可夫决策过程（CMDP），其中自主的gNB智能体在明确满足小区间干扰预算与严格的超可靠低时延通信（URLLC）时延截止期限的同时，最大化频谱效率。我们采用与近端策略优化（PPO）相结合的拉格朗日对偶方法以强化约束条件，同时通过联邦平均实现无需交换原始本地数据的协同学习。在密集多小区环境中的大量仿真表明，SliceFed能够收敛至稳定且具备安全意识的策略。与启发式及无约束基线方法相比，SliceFed实现了对1毫秒URLLC时延截止期限的近100%满足率，并对流量负载变化展现出卓越的鲁棒性，验证了其在可靠且可扩展的6G频谱管理方面的潜力。