Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. FL is effective when dealing with independent and identically distributed (iid) datasets, but struggles with non-iid datasets. Various personalized approaches have been proposed, but such approaches fail to handle underlying shifts in data distribution, such as data distribution skew commonly observed in real-world scenarios (e.g., driver behavior in smart transportation systems changing across time and location). Additionally, trust concerns among unacquainted devices and security concerns with the centralized aggregator pose additional challenges. To address these challenges, this paper presents a dynamically optimized personal deep learning scheme based on blockchain and federated learning. Specifically, the innovative smart contract implemented in the blockchain allows distributed edge devices to reach a consensus on the optimal weights of personalized models. Experimental evaluations using multiple models and real-world datasets demonstrate that the proposed scheme achieves higher accuracy and faster convergence compared to traditional federated and personalized learning approaches.

翻译：联邦学习使边缘设备能够在保护数据隐私的同时协作训练共享模型。在处理独立同分布数据集时联邦学习效果显著，但在非独立同分布数据集上表现不佳。现有多种个性化方法已被提出，但此类方法无法处理数据分布中的潜在偏移——例如真实场景中常见的数据分布倾斜（如智能交通系统中驾驶行为随时间和地理位置变化）。此外，陌生设备间的信任问题及集中式聚合器的安全顾虑也带来了额外挑战。为应对这些挑战，本文提出了一种基于区块链和联邦学习的动态优化个性化深度学习方案。具体而言，区块链中实现的创新性智能合约使分布式边缘设备能够就个性化模型的最优权重达成共识。基于多模型和真实数据集的实验评估表明，与传统联邦学习及个性化学习方法相比，本方案实现了更高的准确率和更快的收敛速度。