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

arXiv: https://arxiv.org/abs/2606.18384 · HTML: https://mlsanity.com/paper/2606.18384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Optimality and Stability in Federated Learning: A Game-theoretic Approach](https://mlsanity.com/paper/2106.09580/) (2021)
- [Context-Aware Online Client Selection for Hierarchical Federated Learning](https://mlsanity.com/paper/2112.00925/) (2021)
- [Fairness-Aware Client Selection for Federated Learning](https://mlsanity.com/paper/2307.10738/) (2023)
- [Client Selection in Federated Learning based on Gradients Importance](https://mlsanity.com/paper/2111.11204/) (2021)
- [Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs](https://mlsanity.com/paper/2401.10361/) (2024)
- [Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks](https://mlsanity.com/paper/2210.14560/) (2022)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game](https://mlsanity.com/paper/2412.15492/) (2024)
- [Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients](https://mlsanity.com/paper/2406.01439/) (2024)
- [Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning](https://mlsanity.com/paper/2409.16495/) (2024)
- [Blockchain-based Framework for Scalable and Incentivized Federated Learning](https://mlsanity.com/paper/2502.14170/) (2025)
- [Federated Learning: From Theory to Practice](https://mlsanity.com/paper/2505.19183/) (2025)

## Abstract (arXiv)

Hierarchical Federated Learning (HFL) enables scalable collaborative model training across distributed devices while preserving data privacy. However, existing HFL client selection mechanisms suffer from a fundamental strategic inefficiency. By prioritizing stability over Pareto efficiency (PE), they produce suboptimal resource allocations, and without strategy proofness (SP), participants are incentivized to misrepresent their true preferences, both failures degrading system overall welfare in the Pareto sense in practice. To address it, we propose SCOPE-FL (Strategy-proof Chain-based Optimal pareto efficient Federated Learning), a synchronous HFL framework that formulates client selection as a two-sided school choice problem solved through the Top Trading Cycle (TTC) algorithm that simultaneously guarantees PE and SP. For reward distribution, SCOPE-FL employs a scalable Shapley value approximation based on One-Round Reconstruction (OR), ensuring compensation proportional to each client's contribution. The entire mechanism executes via blockchain smart contracts, providing the tamper-proof environment required for the SP guarantees to hold in practice. A comprehensive evaluation on MNIST, Fashion-MNIST, and CIFAR-10 demonstrates that SCOPE-FL outperforms state-of-the-art approaches, including DA, IAS, and other methods across model accuracy, convergence rate, and reward efficiency, while achieving communication latency comparable to DA and blockchain overhead significantly lower than DA at scale.