Privacy-Preserving Federated Meta-Learning for Cell-Free Massive MIMO: Instant Adaptation With Distributed Intelligence

The evolution toward 6G wireless networks demands ultra-dense cell-free massive MIMO (Multiple-Input Multiple-Output) systems that can deliver unprecedented connectivity while preserving data privacy and enabling rapid adaptation to dynamic conditions. Current resource allocation approaches rely on centralized deep reinforcement learning frameworks that create scalability bottlenecks, require extensive training periods, and violate emerging privacy regulations through global data aggregation. This paper introduces a Privacy-Preserving Federated Meta-Learning (PP-FML) framework that addresses these fundamental limitations through distributed intelligence and instant adaptation mechanisms. The proposed approach enables each access point to learn optimal resource allocation policies locally while collaboratively improving system-wide performance through cryptographically secure gradient sharing. The meta-learning component provides few-shot adaptation capabilities, allowing networks to respond to new conditions within minutes rather than hours. Comprehensive performance evaluation demonstrates that PP-FML achieves 42.3% sum rate improvement, 28.8% better energy efficiency (15.2 bits/Hz/J), sub-minute adaptation latency (51 seconds), and strong privacy guarantees epsilon 1.0 differential privacy compared to centralized approaches while maintaining complete data privacy and enabling rapid adaptation to changing network conditions. The framework scales linearly to ultra-dense deployments exceeding 300 access points per square kilometer with constant per-node computational complexity, making it suitable for practical 6G network deployment with heterogeneous device populations including mobile users and diverse applications.