  Proof of work (PoW), as the representative consensus protocol for blockchain,
consumes enormous amounts of computation and energy to determine bookkeeping
rights among miners but does not achieve any practical purposes. To address the
drawback of PoW, we propose a novel energy-recycling consensus mechanism named
platform-free proof of federated learning (PF-PoFL), which leverages the
computing power originally wasted in solving hard but meaningless PoW puzzles
to conduct practical federated learning (FL) tasks. Nevertheless, potential
security threats and efficiency concerns may occur due to the untrusted
environment and miners' self-interested features. In this paper, by devising a
novel block structure, new transaction types, and credit-based incentives,
PF-PoFL allows efficient artificial intelligence (AI) task outsourcing,
federated mining, model evaluation, and reward distribution in a fully
decentralized manner, while resisting spoofing and Sybil attacks. Besides,
PF-PoFL equips with a user-level differential privacy mechanism for miners to
prevent implicit privacy leakage in training FL models. Furthermore, by
considering dynamic miner characteristics (e.g., training samples, non-IID
degree, and network delay) under diverse FL tasks, a federation formation
game-based mechanism is presented to distributively form the optimized disjoint
miner partition structure with Nash-stable convergence. Extensive simulations
validate the efficiency and effectiveness of PF-PoFL.
