DeepAI AI Chat
Log In Sign Up

Asynchronous Federated Continual Learning

by   Donald Shenaj, et al.

The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and predefined order. This is not very realistic in federated learning environments where each client works independently in an asynchronous manner getting data for the different tasks in time-frames and orders totally uncorrelated with the other ones. We introduce a novel federated learning setting (AFCL) where the continual learning of multiple tasks happens at each client with different orderings and in asynchronous time slots. We tackle this novel task using prototype-based learning, a representation loss, fractal pre-training, and a modified aggregation policy. Our approach, called FedSpace, effectively tackles this task as shown by the results on the CIFAR-100 dataset using 3 different federated splits with 50, 100, and 500 clients, respectively. The code and federated splits are available at


page 1

page 2

page 3

page 4


Addressing Client Drift in Federated Continual Learning with Adaptive Optimization

Federated learning has been extensively studied and is the prevalent met...

Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning

Federated continual learning (FCL) learns incremental tasks over time fr...

Federated Reconnaissance: Efficient, Distributed, Class-Incremental Learning

We describe federated reconnaissance, a class of learning problems in wh...

Masked Autoencoders are Efficient Continual Federated Learners

Machine learning is typically framed from a perspective of i.i.d., and m...

Exploring Data Redundancy in Real-world Image Classification through Data Selection

Deep learning models often require large amounts of data for training, l...

Better Generative Replay for Continual Federated Learning

Federated learning is a technique that enables a centralized server to l...

Non-IID data and Continual Learning processes in Federated Learning: A long road ahead

Federated Learning is a novel framework that allows multiple devices or ...