There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios. Yet little research has been done regarding the scenario where each client learns on a sequence of tasks from a private local data stream...
Accurate Forgetting for Heterogeneous Federated Continual Learningopenreview.net/forum?id=ShQrnAsbPI code: zaocan666/AF-FCLgithub.com/zaocan666/AF-FCL 简述 Fig.1 example 现有的FCL方法的研究主要假设彻底记住先前任务会产生整体性能上的好处并为此设计了一系列方法,然而在实际中,数据集中通常存在特征偏...
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Federated continual learning110 is considered a viable solution. However, most existing methods are parameter based,111 which can be resource intensive and inefficient when dealing with large-scale models. In contrast, loss-based approaches may be more suitable for FedLLMs. Furthermore, exploring ...
Using continual tasks consisting of various datasets on lightweight architectures, we have validatedthat Pick-a-back provides a significant inference improvementof up to 8.0% via selective knowledge federation. Our codesare available at https://github.com/jinyi-yoon/Pick-a-back.git ....
定义:在持续学习(Continual Learning, CL)领域中,神经网络在适应新任务时往往会破坏之前学习任务的表示,这种现象称为灾难性遗忘。在FL中的表现:由于每轮参与数据分布的变动,FL训练中也会发生灾难性遗忘,导致全局模型在特定轮次的数据分布上过度专业化,而难以泛化。与遗忘的关系:尽管灾难性遗忘看似是遗忘的一种自然方法...
code github.com/MingruiLiu-M Faster federated optimization under second-order similarity 作者Ahmed Khaled 作者 Chi Jin 摘要Federated learning (FL) is a subfield of machine learning where multiple clients try to collaboratively learn a model over a network under communication constraints. We consider fi...
Australiahabibullah.habibullah@unisa.edu.auRyszard KowalczykUniversity of South AustraliaAdelaide, Australiaryszard.kowalczyk@unisa.edu.auABSTRACTFederated Class Incremental Learning (FCIL) is a new direction incontinual learning (CL) for addressing catastrophic forgetting andnon-IID data distribution simultaneo...
Communication-efficient-federated-continual-learning Communication-efficient federated continual learning for distributed learning system with Non-IID data Cite Zhang Z, Zhang Y, Guo D, et al. Communication-efficient federated continual learning for distributed learning system with Non-IID data[J]. Science...
Code: https://github.com/xiyuanyang45/FedAS Abstract (Click to expand): Personalized Federated Learning (PFL) is primarily designed to provide customized models for each client to better fit the non-iid distributed client data, which is a inherent challenge in Federated Learning. However, current...