SCOPE-FL: Novel Federated Learning Framework Combines Blockchain and Game Theory for Optimal Resource Allocation

Researchers have introduced SCOPE-FL, a new hierarchical federated learning system that addresses critical inefficiencies in distributed machine learning by integrating blockchain technology with game-theoretic principles. Hierarchical Federated Learning enables collaborative model training across distributed devices while maintaining data privacy, but existing approaches have struggled with strategic inefficiency in client selection and reward distribution.

The SCOPE-FL framework tackles two fundamental problems in federated learning systems. First, it solves client selection through the Top Trading Cycle algorithm, which formulates the process as a two-sided school choice problem. This approach simultaneously guarantees both Pareto efficiency—ensuring resource allocation cannot be improved for one participant without harming another—and strategy proofness, eliminating incentives for participants to misrepresent their preferences. Second, for reward distribution, SCOPE-FL employs a scalable Shapley value approximation based on One-Round Reconstruction, ensuring each client receives compensation proportional to their actual contribution to model training.

The system executes entirely through blockchain smart contracts, providing the tamper-proof environment necessary to enforce the strategy-proof guarantees in practice. This architectural choice prevents manipulation of selection and reward mechanisms while maintaining transparency across the distributed network. Testing on standard machine learning benchmarks—MNIST, Fashion-MNIST, and CIFAR-10—demonstrated that SCOPE-FL outperformed existing state-of-the-art approaches including DA and IAS methods across multiple performance metrics.

Evaluation results showed improvements in model accuracy, convergence rate, and reward efficiency. Notably, SCOPE-FL achieved communication latency comparable to existing solutions while maintaining significantly lower blockchain overhead at scale, addressing practical deployment concerns. The framework represents a significant advance in making federated learning systems both theoretically sound and practically implementable for large-scale collaborative machine learning applications.