Communication-Constrained Private Decentralized Online Personalized Mean Estimation
This work addresses privacy-preserving decentralized learning for multiple agents with limited communication, though it appears incremental with specific theoretical and numerical validations.
The paper tackles the problem of collaborative personalized mean estimation under communication constraints and differential privacy, showing that a consensus-based algorithm achieves faster convergence than fully local approaches under certain conditions.
We consider the problem of communication-constrained collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. A consensus-based algorithm is studied under the framework of differential privacy in order to protect each agent's data. We give a theoretical convergence analysis of the proposed consensus-based algorithm for any bounded unknown distributions on the agents' data, showing that collaboration provides faster convergence than a fully local approach where agents do not share data, under an oracle decision rule and under some restrictions on the privacy level and the agents' connectivity, which illustrates the benefit of private collaboration in an online setting under a communication restriction on the agents. The theoretical faster-than-local convergence guarantee is backed up by several numerical results.