Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adapt to Adaptation: Learning Personalization for Cross-Silo Federated
  Learning   Conventional federated learning (FL) trains one global model for a federation
of clients with decentralized data, reducing the privacy risk of centralized
training. However, the distribution shift across non-IID datasets, often poses
a challenge to this one-model-fits-all solution. Personalized FL aims to
mitigate this issue systematically. In this work, we propose APPLE, a
personalized cross-silo FL framework that adaptively learns how much each
client can benefit from other clients' models. We also introduce a method to
flexibly control the focus of training APPLE between global and local
objectives. We empirically evaluate our method's convergence and generalization
behaviors, and perform extensive experiments on two benchmark datasets and two
medical imaging datasets under two non-IID settings. The results show that the
proposed personalized FL framework, APPLE, achieves state-of-the-art
performance compared to several other personalized FL approaches in the
literature. The code is publicly available at
https://github.com/ljaiverson/pFL-APPLE.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics