标题： FedBKD：在非独立同分布数据上实现泛化与个性化的知识蒸馏联邦学习 显示英文标题

标题： FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data

摘要： 联邦学习（FL）是一种去中心化的协同机器学习（ML）技术。 它为工业ML实践中的孤立数据孤岛和数据隐私泄露问题提供了解决方案。 FL中的一个主要挑战是处理非独立同分布（non-IID）的数据。 当前的解决方案要么专注于构建一个全能的全局模型，要么定制个性化的本地模型。 很少有方法能同时提供一个泛化良好的全局模型和表现良好的本地模型。 此外，许多解决non-IID问题的FL方案得益于引入公共数据集。 然而，这也会增加数据泄露的风险。 为了解决这些问题，我们提出了一种新颖的无数据蒸馏框架，联邦双向知识蒸馏（FedBKD）。 具体来说，我们训练生成对抗网络（GAN）来生成合成数据。 在GAN训练过程中，本地模型作为判别器，其参数被冻结。 然后将合成数据用于全局模型和本地模型之间的双向蒸馏，以实现知识交互，从而提升双方的性能。 我们在不同non-IID设置下的4个基准测试中进行了广泛的实验。 结果表明，FedBKD在每种情况下都达到了最先进的性能。 显示英文摘要

摘要： Federated learning (FL) is a decentralized collaborative machine learning (ML) technique. It provides a solution to the issues of isolated data islands and data privacy leakage in industrial ML practices. One major challenge in FL is handling the non-identical and independent distributed (non-IID) data. Current solutions either focus on constructing an all-powerful global model, or customizing personalized local models. Few of them can provide both a well-generalized global model and well-performed local models at the same time. Additionally, many FL solutions to the non-IID problem are benefited from introducing public datasets. However, this will also increase the risk of data leakage. To tackle the problems, we propose a novel data-free distillation framework, Federated Bidirectional Knowledge Distillation (FedBKD). Specifically, we train Generative Adversarial Networks (GAN) for synthetic data. During the GAN training, local models serve as discriminators and their parameters are frozen. The synthetic data is then used for bidirectional distillation between global and local models to achieve knowledge interactions so that performances for both sides are improved. We conduct extensive experiments on 4 benchmarks under different non-IID settings. The results show that FedBKD achieves SOTA performances in every case.