In this paper, we present a study of a Federated Learning (FL) system, based on the use of decentralized architectures to ensure trust and increase reliability. The system is based on the idea that the FL collaborators upload the (ciphered) model parameters on the Inter-Planetary File System (IPFS) and interact with a dedicated smart contract to track their behavior. Thank to this smart contract, the phases of parameter updates are managed efficiently, thereby strengthening data security. We have carried out an experimental study that exploits two different methods of weight aggregation, i.e., a classic averaging scheme and a federated proximal aggregation. The results confirm the feasibility of the proposal.

翻译：本文提出了一种基于去中心化架构的联邦学习系统研究，旨在确保信任并提升系统可靠性。该系统基于以下设计理念：联邦学习协作方将（加密后的）模型参数上传至星际文件系统，并通过专用智能合约记录其行为轨迹。借助该智能合约，参数更新阶段得以高效管理，从而增强数据安全性。我们通过实验研究比较了两种权重聚合方法——经典平均聚合与联邦近端聚合。实验结果验证了该方案的可行性。