Optimizing Federated Learning Performance: A Blockchain-Integrated Solution for Edge Networks

This paper proposes a blockchain-integrated federated learning (FL) framework tailored for secure, efficient, and energy-aware model training in edge computing environments. The framework follows an offload-train-aggregate paradigm where edge devices transmit local datasets to proximate servers for localized model updates. A reputation-driven RAFT consensus protocol is incorporated to achieve reliable, low-latency, and lightweight blockchain coordination while preserving privacy and accountability. To overcome the inherent mixed-integer nonlinear programming (MINLP) complexity, we develop a two-stage cross-layer optimization strategy. In the first stage, an alternating direction method of multipliers (ADMM)-based feedback control scheme jointly allocates bandwidth and computation resources under energy and delay constraints. In the second stage, server selection and sub-band assignment are modeled as a bipartite matching problem and solved via the Hungarian algorithm, guided by a convergence-aware performance bound. Extensive simulations demonstrate that our framework significantly improves learning accuracy, uplink throughput, and energy efficiency over state-of-the-art FL baselines. It also exhibits strong robustness to network fragmentation and resource heterogeneity, making it well suited for practical edge environments.