We focus on the problem of Personalized Federated Continual Learning (PFCL):
a group of distributed clients, each with a sequence of local tasks on
arbitrary data distributions, collaborate through a central server to train a
personalized model at each client, with the model expected to achieve good
performance on all local tasks. We propose a novel PFCL framework called
Federated Memory Strengthening FedMeS to address the challenges of client drift
and catastrophic forgetting. In FedMeS, each client stores samples from
previous tasks using a small amount of local memory, and leverages this
information to both 1) calibrate gradient updates in training process; and 2)
perform KNN-based Gaussian inference to facilitate personalization. FedMeS is
designed to be task-oblivious, such that the same inference process is applied
to samples from all tasks to achieve good performance. FedMeS is analyzed
theoretically and evaluated experimentally. It is shown to outperform all
baselines in average accuracy and forgetting rate, over various combinations of
datasets, task distributions, and client numbers.

个性化联合持续学习（PFCL）的问题是一个分布式客户端组，每个客户端都有一系列的本地任务和任意数据分布，通过一个中央服务器合作训练每个客户端的个性化模型，而这个模型需要在所有本地任务中表现良好。我们提出了一个名为联合记忆增强 FedMeS 的新型 PFCL 框架，以解决客户漂移和灾难性遗忘的挑战。在 FedMeS 中，每个客户端使用少量本地内存来存储来自先前任务的样本，并利用这些信息来同时：1) 校准训练过程中的梯度更新；2) 执行基于 KNN 的高斯推断以促进个性化。FedMeS 被设计为任务无关的，这意味着相同的推断过程适用于所有任务的样本，以取得好的性能。FedMeS 在理论上进行了分析并进行了实验评估。实验证明，在各种数据集、任务分布和客户端数量的组合中，它在平均准确性和遗忘率方面优于所有基线模型。