Federated optimization, wherein several agents in a network collaborate with a central server to achieve optimal social cost over the network with no requirement for exchanging information among agents, has attracted significant interest from the research community. In this context, agents demand resources based on their local computation. Due to the exchange of optimization parameters such as states, constraints, or objective functions with a central server, an adversary may infer sensitive information of agents. We develop LDP-AIMD, a local differentially-private additive-increase and multiplicative-decrease (AIMD) algorithm, to allocate multiple divisible shared resources to agents in a network. The LDP-AIMD algorithm provides a differential privacy guarantee to agents in the network. No inter-agent communication is required; however, the central server keeps track of the aggregate consumption of resources. We present experimental results to check the efficacy of the algorithm. Moreover, we present empirical analyses for the trade-off between privacy and the efficiency of the algorithm.

翻译：联邦优化是指网络中多个智能体与中央服务器协作，在无需智能体间交换信息的情况下实现网络最优社会成本的方法，这一方向已引起研究界的广泛关注。在此框架下，智能体基于本地计算请求资源。由于智能体与中央服务器之间会交换状态、约束条件或目标函数等优化参数，敌手可能推断出智能体的敏感信息。我们提出了LDP-AIMD算法——一种基于本地差分隐私的加性增乘性减（AIMD）算法，用于将多个可分割的共享资源分配给网络中的智能体。该算法为网络中的智能体提供了差分隐私保障，且无需智能体间通信，但中央服务器会追踪资源的总消耗量。我们通过实验验证了算法的有效性，并进一步分析了算法在隐私保护与效率之间的权衡关系。