Federated Learning (FL) under distributed concept drift is a largely
unexplored area. Although concept drift is itself a well-studied phenomenon, it
poses particular challenges for FL, because drifts arise staggered in time and
space (across clients). To the best of our knowledge, this work is the first to
explicitly study data heterogeneity in both dimensions. We first demonstrate
that prior solutions to drift adaptation that use a single global model are
ill-suited to staggered drifts, necessitating multiple-model solutions. We
identify the problem of drift adaptation as a time-varying clustering problem,
and we propose two new clustering algorithms for reacting to drifts based on
local drift detection and hierarchical clustering. Empirical evaluation shows
that our solutions achieve significantly higher accuracy than existing
baselines, and are comparable to an idealized algorithm with oracle knowledge
of the ground-truth clustering of clients to concepts at each time step.

本文研究了分布式概念漂移下的联邦学习，提出了两种新的聚类算法以适应数据异质性和时间变化的漂移，并且通过实验验证表明它们的准确性显著优于现有的基线算法，接近理想算法。