Autonomous Federated Learning: Incentive-Aligned, Energy-Aware, and Byzantine-Resilient Coordination

Federated learning (FL) assumes clients are cooperative, honest, and energy-agnostic. These assumptions rarely hold in real deployments where participants own private data, operate under tight energy budgets, and face misaligned incentives. We address this gap by introducing Autonomous Federated Learning (AFL), an autonomous framework in which each client runs an agent that solves a local constrained decision problem to jointly optimize participation, update effort, and verification actions under explicit energy limits. To align individual incentives with collective progress, we propose Proof-of-Useful Learning (PoUL): a reward mechanism that compensates clients in proportion to verifiable utility gain per unit energy, where utility is assessed via committee-based validation on a shared reference set and energy is estimated through hardware-aware computation models. AFL maintains a persistent trust ledger encoding each client’s contribution history and efficiency profile, ensuring that only reliable, energy-efficient participants accumulate long-term influence. Across CIFAR-10, FMNIST, and MNIST under heterogeneous and adversarial settings with 30% Byzantine clients, AFL achieves a worst-case accuracy degradation of only 3.93% on MNIST and 20.17% on CIFAR-10, compared to 13.52% and 46.48% for the strongest aggregation baseline (Fed-NGA), while methods such as CClip, MCA, and Geometric Median collapse to near-chance accuracy under structured attacks. AFL’s trust ledger reliably isolates adversarial clients, assigning them confidence scores ≤ 0.15 versus a benign-client mean of 0.75, effectively removing them from future participation. AFL improves utility-per-Joule by 15–35% over random client selection, results demonstrate a practical path toward FL that is simultaneously incentive compatible and robust without requiring computationally wasteful consensus proofs