论文标题
分解FL：不可知的个性化联合学习，内核分解和相似性匹配
Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching
论文作者
论文摘要
在现实世界中联合的学习场景中，由于使用不同的标签排列或解决完全不同的任务或域，参与者可以拥有自己的个性化标签，这些标签与其他客户不兼容。但是，大多数现有的FL方法无法有效地应对如此异构的场景，因为他们经常假设（1）所有参与者都使用同步的标签集，并且（2）他们从同一域中训练相同的任务。在这项工作中，为了应对这些挑战，我们介绍了分解的FL，它允许通过将模型参数分配到一对向量中，可以有效地解决标签和任务 - 异构联合学习设置，其中一个人捕获了跨不同标签和任务的常识，而其他人则捕获了每个本地模型识别的知识。此外，根据客户端特定的向量空间的距离，分解FL执行选择性聚合方案，仅利用每个客户的相关参与者的知识。我们在标签和域的异构设置上广泛验证了我们的方法，在该设置上，它的表现优于最先进的个性化联合学习方法。
In real-world federated learning scenarios, participants could have their own personalized labels which are incompatible with those from other clients, due to using different label permutations or tackling completely different tasks or domains. However, most existing FL approaches cannot effectively tackle such extremely heterogeneous scenarios since they often assume that (1) all participants use a synchronized set of labels, and (2) they train on the same task from the same domain. In this work, to tackle these challenges, we introduce Factorized-FL, which allows to effectively tackle label- and task-heterogeneous federated learning settings by factorizing the model parameters into a pair of vectors, where one captures the common knowledge across different labels and tasks and the other captures knowledge specific to the task each local model tackles. Moreover, based on the distance in the client-specific vector space, Factorized-FL performs selective aggregation scheme to utilize only the knowledge from the relevant participants for each client. We extensively validate our method on both label- and domain-heterogeneous settings, on which it outperforms the state-of-the-art personalized federated learning methods.
