BETTER GENERATIVE REPLAY FOR CONTINUAL FEDERATED LEARNING

Abstract

Federated Learning (FL) aims to develop a centralized server that learns from distributed clients via communications without accessing the clients' local data. However, existing works mainly focus on federated learning in a single task scenario. with static data. In this paper, we introduce the continual federated learning (CFL) problem, where clients incrementally learn new tasks and history data cannot be stored due to certain reasons, such as limited storage and data retention policy 1 . Generative replay (GR) based methods are effective for continual learning without storing history data. However, we fail when trying to intuitively adapt GR models for this setting. By analyzing the behaviors of clients during training, we find the unstable training process caused by distributed training on non-IID data leads to a notable performance degradation. To address this problem, we propose our FedCIL model with two simple but effective solutions: 1. model consolidation and 2. consistency enforcement. Experimental results on multiple benchmark datasets demonstrate that our method significantly outperforms baselines.

1. INTRODUCTION

Federated learning (McMahan et al., 2017) is an emerging topic in machine learning, where a powerful global model is maintained via communications with distributed clients without access to their local data. A typical challenge in federated learning is the non-IID data distribution (Zhao et al., 2018; Zhu et al., 2021a) , where the data distributions learnt by different clients are different (known as heterogeneous federated learning). Recent methods (Li et al., 2020; Chen & Chao, 2020; Zhu et al., 2021b) gain improvements in the typical federated learning setting, where the global model is learning a single task and each client is trained locally on fixed data. However, in real-world applications, it is more practical that each client is continuously learning new tasks. Traditional federated learning models fail to solve this problem. In practice, history data are sometimes inaccessible considering privacy constraints (e.g., data protection under GDPR) or limited storage space (e.g., mobile devices with very limited space), and the unavailability of previous data often leads to catastrophic forgetting (McCloskey & Cohen, 1989) in many machine learning models. Continual learning (Thrun, 1995; Kumar & Daume III, 2012; Ruvolo & Eaton, 2013; Chu & Li, 2023) aims to develop an intelligent system that can continuously learn from new tasks without forgetting learnt knowledge in the absence of previous data. Common continual learning scenarios can be roughly divided into two scenarios (Van de Ven & Tolias, 2019): task incremental learning (TIL) and class incremental learning (CIL) (Rebuffi et al., 2017) . In both scenarios, the intelligent system is required to solve all tasks so far. In TIL, task-IDs of different task are accessible, while in CIL, they are unavailable, which requires the system to infer task-IDs. The unavailability of task-IDs makes the problem significantly harder. In this paper, we propose a challenging and realistic problem, continual federated learning. More specifically, we aim to deal with the class-incremental federated learning (CI-FL) problem. In this setting, each client is continuously learning new classes from a sequence of tasks, and the centralized server learns from the clients via communications. It is more difficult than the single FL or CIL,



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availability

https://github.com/daiqing98/FedCIL.

