Personalized FL has been widely used to cater to heterogeneity challenges with non-IID data. A primary obstacle is considering the personalization process from the client's perspective to preserve their autonomy. Allowing the clients to participate in personalized FL decisions becomes significant due to privacy and security concerns, where the clients may not be at liberty to share private information necessary for producing good quality personalized models. Moreover, clients with high-quality data and resources are reluctant to participate in the FL process without reasonable incentive. In this paper, we propose PI-FL, a one-shot personalization solution complemented by a token-based incentive mechanism that rewards personalized training. PI-FL outperforms other state-of-the-art approaches and can generate good-quality personalized models while respecting clients' privacy.

翻译：个性化联邦学习已被广泛用于应对非独立同分布数据带来的异构性挑战。其中一个主要障碍在于从客户端视角考虑个性化过程以维护其自主性。由于隐私和安全问题，允许客户端参与个性化联邦学习决策变得至关重要——客户端可能无法自由共享生成高质量个性化模型所需的私有信息。此外，拥有高质量数据和资源的客户端若无合理激励，往往不愿参与联邦学习过程。本文提出PI-FL，这是一种融合基于代币激励机制的单次个性化解决方案，该机制可为个性化训练提供奖励。PI-FL在生成高质量个性化模型的同时尊重客户端隐私，性能优于其他现有先进方法。