  Federated Learning (FL) has emerged as a de facto machine learning area and
received rapid increasing research interests from the community. However,
catastrophic forgetting caused by data heterogeneity and partial participation
poses distinctive challenges for FL, which are detrimental to the performance.
To tackle the problems, we propose a new FL approach (namely GradMA), which
takes inspiration from continual learning to simultaneously correct the
server-side and worker-side update directions as well as take full advantage of
server's rich computing and memory resources. Furthermore, we elaborate a
memory reduction strategy to enable GradMA to accommodate FL with a large scale
of workers. We then analyze convergence of GradMA theoretically under the
smooth non-convex setting and show that its convergence rate achieves a linear
speed up w.r.t the increasing number of sampled active workers. At last, our
extensive experiments on various image classification tasks show that GradMA
achieves significant performance gains in accuracy and communication efficiency
compared to SOTA baselines.
