SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911507020775424 |
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| author | Mohammadi, Hossein Natanzi, Seyed Bagher Hashemi Nassiri, Ramak Hassanpour, Jamshid Tang, Bo Marojevic, Vuk |
| author_facet | Mohammadi, Hossein Natanzi, Seyed Bagher Hashemi Nassiri, Ramak Hassanpour, Jamshid Tang, Bo Marojevic, Vuk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11390 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G Mohammadi, Hossein Natanzi, Seyed Bagher Hashemi Nassiri, Ramak Hassanpour, Jamshid Tang, Bo Marojevic, Vuk Networking and Internet Architecture Systems and Control 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. |
| title | SliceFed: Federated Constrained Multi-Agent DRL for Dynamic Spectrum Slicing in 6G |
| topic | Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2603.11390 |