SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System

A new hierarchical federated learning framework called SCOPE-FL uses a school-choice algorithm and blockchain smart contracts to guarantee both Pareto efficiency and strategy proofness in client selection, according to a paper submitted to arXiv on 16 Jun 2026 [1]. The paper, posted on the open-access preprint repository arXiv, addresses a long-standing problem in Hierarchical Federated Learning (HFL): existing client selection mechanisms prioritize stability over Pareto efficiency, producing suboptimal resource allocations, and lack strategy proofness, which incentivizes participants to misrepresent their true preferences [1][2]. The authors propose SCOPE-FL, which stands for Strategy-proof Chain-based Optimal pareto efficient Federated Learning, a synchronous HFL framework that reformulates client selection as a two-sided school choice problem [1][2]. It solves this problem through the Top Trading Cycle (TTC) algorithm, a mechanism that simultaneously guarantees both Pareto efficiency and strategy proofness [1][2]. For reward distribution, the system employs a scalable Shapley value approximation based on One-Round Reconstruction, ensuring compensation proportional to each client's contribution [1][2]. The entire mechanism executes via blockchain smart contracts, providing the tamper-proof environment required for the strategy-proofness guarantees to hold in practice [1][2]. The authors evaluated SCOPE-FL on three standard image classification datasets: MNIST, Fashion-MNIST, and CIFAR-10 [1][2]. The results show that SCOPE-FL outperforms state-of-the-art approaches, including DA and IAS, across model accuracy, convergence rate, and reward efficiency [1][2]. At scale, the system achieves communication latency comparable to DA while incurring blockchain overhead significantly lower than DA [1][2]. arXiv, which began on August 14, 1991, serves as an open-access repository of electronic preprints and postprints that are moderated but not peer reviewed, and as of November 2024 receives about 24,000 submissions per month [6]. The paper appears on the repository with the standard arXiv Labs integrations, a framework launched in 2020 that allows community collaborators to develop and share experimental tools directly on article record pages [4][5].