Federated learning (FL) is a type of collaborative machine learning where
participating peers/clients process their data locally, sharing only updates to
the collaborative model. This enables to build privacy-aware distributed
machine learning models, among others. The goal is the optimization of a
statistical model's parameters by minimizing a cost function of a collection of
datasets which are stored locally by a set of clients. This process exposes the
clients to two issues: leakage of private information and lack of
personalization of the model. On the other hand, with the recent advancements
in various techniques to analyze data, there is a surge of concern for the
privacy violation of the participating clients. To mitigate this, differential
privacy and its variants serve as a standard for providing formal privacy
guarantees. Often the clients represent very heterogeneous communities and hold
data which are very diverse. Therefore, aligned with the recent focus of the FL
community to build a framework of personalized models for the users
representing their diversity, it is also of utmost importance to protect the
clients' sensitive and personal information against potential threats. To
address this goal we consider $d$-privacy, also known as metric privacy, which
is a variant of local differential privacy, using a a metric-based obfuscation
technique that preserves the topological distribution of the original data. To
cope with the issue of protecting the privacy of the clients and allowing for
personalized model training to enhance the fairness and utility of the system,
we propose a method to provide group privacy guarantees exploiting some key
properties of $d$-privacy which enables personalized models under the framework
of FL. We provide theoretical justifications to the applicability and
experimental validation on real datasets to illustrate the working of our
method.

该研究论文探讨了在联邦学习中使用 $d$-privacy 的方法，以保护客户的敏感信息并允许个性化模型训练以提高系统的公平性和效用。同时，使用度量基础的混淆技术来保持原始数据的拓扑分布特征，并在真实数据集上进行了证明和实验验证。