This work studies the intersection of continual and federated learning, in which independent agents face unique tasks in their environments and incrementally develop and share knowledge. We introduce a mathematical framework capturing the essential aspects of distributed continual learning, including agent model and statistical heterogeneity, continual distribution shift, network topology, and communication constraints. Operating on the thesis that distributed continual learning enhances individual agent performance over single-agent learning, we identify three modes of information exchange: data instances, full model parameters, and modular (partial) model parameters. We develop algorithms for each sharing mode and conduct extensive empirical investigations across various datasets, topology structures, and communication limits. Our findings reveal three key insights: sharing parameters is more efficient than sharing data as tasks become more complex; modular parameter sharing yields the best performance while minimizing communication costs; and combining sharing modes can cumulatively improve performance.

分布式继续学习研究了多智能体在其环境中独立面对独特任务并逐步开发和分享知识的交叉点。通过引入数学框架，捕捉了分布式继续学习的关键方面，包括智能体模型和统计异质性、持续分布转移、网络拓扑和通信限制。我们提出了三种信息交换模式：数据实例，完整模型参数和模块化（部分）模型参数，并对每种共享模式开发了相应的算法，并在各种数据集、拓扑结构和通信限制下进行了广泛的实证研究。我们的研究结果揭示了三个关键见解：随着任务的复杂化，参数共享比数据共享更加高效；模块化参数共享在最小化通信成本的同时，提供了最佳性能；结合多种共享模式可以累积改善性能。

分布式持续学习