Federated Class-Incremental Learning
Federated learning (FL) has attracted growing attention via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume object classes of the overall framework are fixed over time. It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes. Moreover, new clients with unseen new classes may participate in the FL training, further aggravating the catastrophic forgetting of the global model. To address these challenges, we develop a novel Global-Local Forgetting Compensation (GLFC) model, to learn a global class incremental model for alleviating the catastrophic forgetting from both local and global perspectives. Specifically, to address local forgetting caused by class imbalance at the local clients, we design a class-aware gradient compensation loss and a class-semantic relation distillation loss to balance the forgetting of old classes and distill consistent inter-class relations across tasks. To tackle the global forgetting brought by the non-i.i.d class imbalance across clients, we propose a proxy server that selects the best old global model to assist the local relation distillation. Moreover, a prototype gradient-based communication mechanism is developed to protect privacy. Our model outperforms state-of-the-art methods by 4.4%-15.1% in terms of average accuracy on representative benchmark datasets.
Code (1)
Tasks
class-incremental learningClass Incremental LearningFederated LearningIncremental LearningSimilar Papers 제목 키워드 기반
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory
Federated Class Incremental Learning (FCIL) is a critical yet largely underexplored issue that deals with the dynamic incorporation of new classes within federated learning (FL). Existing methods often employ generative …
class-incremental learningClass Incremental LearningFederated LearningIncremental Learning+2Federated Class-Incremental Learning with Prompting
As Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread attention due to its ability to protect data…
class-incremental learningClass Incremental LearningFederated LearningIncremental LearningPILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning
Existing federated learning methods have effectively dealt with decentralized learning in scenarios involving data privacy and non-IID data. However, in real-world situations, each client dynamically learns new classes, …
class-incremental learningClass Incremental LearningFederated LearningIncremental LearningFederated Class-Incremental Learning with New-Class Augmented Self-Distillation
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data. Mainstream FL methodologies overlook the dynamic nature of real-world data, particularly its ten…
class-incremental learningClass Incremental LearningFederated LearningIncremental Learning+1FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer
Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing methods do not consider the trustworthines…
class-incremental learningClass Incremental LearningFederated LearningIncremental Learning+1