Abstract Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue
involving the dynamic addition of new classes in the context of federated learning. In this …

Existing federated learning methods have effectively addressed decentralized learning in
scenarios involving data privacy and non-IID data. However, in real-world situations, each …

Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. On top of that,
Exemplar-free Class Incremental Learning is even more challenging due to forbidden …

Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large
language models (MLLMs), where improving performance on unseen tasks often leads to a …

Abstract Federated Class-Incremental Learning (FCIL) focuses on continually transferring
the previous knowledge to learn new classes in dynamic Federated Learning (FL). However …

Federated learning (FL), as a distributed machine learning paradigm, enables on-device
model training and inference without data updates or privacy breaches, promoting edge …

Federated Learning (FL) seeks to train a model collaboratively without sharing private
training data from individual clients. Despite its promise, FL encounters challenges such as …

Federated Learning (FL) aims at unburdening the training of deep models by distributing
computation across multiple devices (clients) while safeguarding data privacy. On top of that …

Federated Class-Incremental Learning (FCIL) aims to allow federated learning (FL) systems
to consistently learn new tasks with classes that change dynamically, without forgetting …

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) …