The growing adoption of Artificial Intelligence (AI) in Internet of Things (IoT) ecosystems has intensified the need for personalized learning methods that can operate efficiently and privately across heterogeneous, resource-constrained devices. However, enabling effective personalized learning in decentralized settings introduces several challenges, including efficient knowledge transfer between clients, protection of data privacy, and resilience against poisoning attacks. In this paper, we address these challenges by developing P4 (Personalized, Private, Peer-to-Peer) -- a method designed to deliver personalized models for resource-constrained IoT devices while ensuring differential privacy and robustness against poisoning attacks. Our solution employs a lightweight, fully decentralized algorithm to privately detect client similarity and form collaborative groups. Within each group, clients leverage differentially private knowledge distillation to co-train their models, maintaining high accuracy while ensuring robustness to the presence of malicious clients. We evaluate P4 on popular benchmark datasets using both linear and CNN-based architectures across various heterogeneity settings and attack scenarios. Experimental results show that P4 achieves 5% to 30% higher accuracy than leading differentially private peer-to-peer approaches and maintains robustness with up to 30% malicious clients. Additionally, we demonstrate its practicality by deploying it on resource-constrained devices, where collaborative training between two clients adds only ~7 seconds of overhead.

翻译：物联网生态系统中人工智能的日益普及，强化了对能够在异构、资源受限设备上高效且私密运行的个性化学习方法的需求。然而，在去中心化环境中实现有效的个性化学习带来了若干挑战，包括客户端间的高效知识迁移、数据隐私保护以及对投毒攻击的抵御能力。本文通过开发P4（个性化、私有化、点对点）方法应对这些挑战——该方法旨在为资源受限的物联网设备提供个性化模型，同时确保差分隐私和对投毒攻击的鲁棒性。我们的解决方案采用轻量级、完全去中心化的算法来私密地检测客户端相似性并形成协作组。在每个组内，客户端利用差分隐私知识蒸馏技术协同训练其模型，在保持高精度的同时确保对恶意客户端存在的鲁棒性。我们在多种异构设置和攻击场景下，使用线性及基于CNN的架构，在流行基准数据集上评估了P4。实验结果表明，相较于领先的差分隐私点对点方法，P4实现了5%至30%的精度提升，并在恶意客户端比例高达30%时仍保持鲁棒性。此外，我们通过在资源受限设备上的部署验证了其实用性，其中两个客户端间的协同训练仅增加约7秒的开销。