Distributed (or Federated) learning enables users to train machine learning models on their very own devices, while they share only the gradients of their models usually in a differentially private way (utility loss). Although such a strategy provides better privacy guarantees than the traditional centralized approach, it requires users to blindly trust a centralized infrastructure that may also become a bottleneck with the increasing number of users. In this paper, we design and implement P4L: a privacy preserving peer-to-peer learning system for users to participate in an asynchronous, collaborative learning scheme without requiring any sort of infrastructure or relying on differential privacy. Our design uses strong cryptographic primitives to preserve both the confidentiality and utility of the shared gradients, a set of peer-to-peer mechanisms for fault tolerance and user churn, proximity and cross device communications. Extensive simulations under different network settings and ML scenarios for three real-life datasets show that P4L provides competitive performance to baselines, while it is resilient to different poisoning attacks. We implement P4L and experimental results show that the performance overhead and power consumption is minimal (less than 3mAh of discharge).

翻译：分布式（或联邦）学习使用户能够在自己设备上训练机器学习模型，同时通常以差分隐私方式（牺牲效用）仅共享模型梯度。尽管此类策略比传统集中式方法提供更好的隐私保障，但它要求用户盲目信任可能随着用户数量增加而成为瓶颈的集中式基础设施。本文设计并实现了P4L：一种隐私保护的对等学习系统，使用户能够在无需任何基础设施或依赖差分隐私的情况下参与异步协作学习方案。我们的设计采用强密码学原语来保护共享梯度的机密性与效用，并构建包含容错与用户动态管理、邻近性及跨设备通信的对等机制。针对三个真实数据集在不同网络设置与机器学习场景下的广泛仿真表明，P4L在保持与基线方法相当性能的同时，对多种投毒攻击具有鲁棒性。我们实现了P4L，实验结果表明其性能开销与功耗极低（放电量小于3mAh）。