Coalition Formation for Heterogeneous Federated Learning Enabled Channel Estimation in RIS-assisted Cell-free MIMO

2025-02-08

AI Summary

To address the three critical bottlenecks—excessive communication overhead, user privacy leakage, and limited on-device computational capability—in high-dimensional cascaded channel estimation for RIS-aided cell-free MIMO systems, this paper proposes a heterogeneous federated learning framework based on coalition formation. The framework integrates distributed deep reinforcement learning for dynamic network orchestration and introduces a novel dual similarity metric—incorporating both geometric distance and signal power—to drive transfer learning and accelerate model convergence. Compared with conventional centralized deep learning, the proposed scheme reduces on-device computational overhead by 16%, improves channel estimation accuracy by 20%, and ensures data remain localized during training, thereby significantly enhancing privacy preservation. To the best of our knowledge, this is the first federated intelligence solution tailored for RIS-assisted cell-free MIMO channel estimation that jointly addresses system heterogeneity, scalability, and privacy security.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Downlink channel estimation remains a significant bottleneck in reconfigurable intelligent surface-assisted cell-free multiple-input multiple-output communication systems. Conventional approaches primarily rely on centralized deep learning methods to estimate the high-dimensional and complex cascaded channels. These methods require data aggregation from all users for centralized model training, leading to excessive communication overhead and significant data privacy concerns. Additionally, the large size of local learning models imposes heavy computational demands on end users, necessitating strong computational capabilities that most commercial devices lack. To address the aforementioned challenges, a coalition-formation-guided heterogeneous federated learning (FL) framework is proposed. This framework leverages coalition formation to guide the formation of heterogeneous FL user groups for efficient channel estimation. Specifically, by utilizing a distributed deep reinforcement learning (DRL) approach, each FL user intelligently and independently decides whether to join or leave a coalition, aiming at improving channel estimation accuracy, while reducing local model size and computational costs for end users. Moreover, to accelerate the DRL-FL convergence process and reduce computational burdens on end users, a transfer learning method is introduced. This method incorporates both received reference signal power and distance similarity metrics, by considering that nodes with similar distances to the base station and comparable received signal power have a strong likelihood of experiencing similar channel fading. Massive experiments performed that reveal that, compared with the benchmarks, the proposed framework significantly reduces the computational overhead of end users by 16%, improves data privacy, and improves channel estimation accuracy by 20%.

Problem

• Enhance channel estimation accuracy
• Reduce computational overhead
• Improve data privacy

Innovation

• Coalition-guided heterogeneous FL
• Distributed DRL decision-making
• Transfer learning accelerates convergence