Data privacy and silos are nontrivial and greatly challenging in many
real-world applications. Federated learning is a decentralized approach to
training models across multiple local clients without the exchange of raw data
from client devices to global servers. However, existing works focus on a
static data environment and ignore continual learning from streaming data with
incremental tasks. Federated Continual Learning (FCL) is an emerging paradigm
to address model learning in both federated and continual learning
environments. The key objective of FCL is to fuse heterogeneous knowledge from
different clients and retain knowledge of previous tasks while learning on new
ones. In this work, we delineate federated learning and continual learning
first and then discuss their integration, i.e., FCL, and particular FCL via
knowledge fusion. In summary, our motivations are four-fold: we (1) raise a
fundamental problem called ”spatial-temporal catastrophic forgetting” and
evaluate its impact on the performance using a well-known method called
federated averaging (FedAvg), (2) integrate most of the existing FCL methods
into two generic frameworks, namely synchronous FCL and asynchronous FCL, (3)
categorize a large number of methods according to the mechanism involved in
knowledge fusion, and finally (4) showcase an outlook on the future work of
FCL.
The Significance of Federated Continual Learning in Multi-Disciplinary Applications
Data privacy and the management of siloed information are complex challenges in various real-world applications. Traditional approaches to training machine learning models typically require the exchange of raw data from client devices to global servers, raising concerns about privacy and security. In response to these concerns, federated learning has emerged as a decentralized approach that enables model training across multiple local clients without sharing sensitive data.
However, existing federated learning methods have mostly focused on static data environments and overlook the importance of continual learning from streaming data with incremental tasks. This is where Federated Continual Learning (FCL) comes into play. FCL combines the principles of federated learning and continual learning to enable model learning in both federated and continual learning environments.
The primary objective of FCL is twofold: to merge heterogeneous knowledge from different clients and to retain knowledge from previous tasks while learning new ones. By doing so, FCL addresses the challenge of “spatial-temporal catastrophic forgetting,” a fundamental problem that arises when a model forgets previously learned knowledge as it learns new tasks.
In this work, the authors outline the concepts of federated learning and continual learning before exploring their integration through FCL. They introduce two generic frameworks for FCL: synchronous FCL and asynchronous FCL. These frameworks encompass most existing FCL methods and provide a structured approach to categorize them based on the mechanism involved in knowledge fusion.
Furthermore, the authors highlight the importance of evaluating the impact of spatial-temporal catastrophic forgetting on model performance using a widely used method called federated averaging (FedAvg). This analysis sheds light on the limitations of existing approaches and serves as a starting point for developing more robust FCL techniques.
Looking ahead, the authors provide an outlook on future work in the field of FCL. They emphasize the need for research on efficient knowledge transfer mechanisms, privacy-preserving techniques, and the adaptation of FCL to different application domains. The multi-disciplinary nature of FCL becomes evident as it requires expertise in machine learning, privacy and security, and data management.
In conclusion, Federated Continual Learning (FCL) is a promising paradigm that integrates federated learning and continual learning to address the challenges of model training in dynamic data environments. The development of FCL frameworks and the categorization of existing methods pave the way for further advancements in this field. As researchers delve deeper into the integration of knowledge fusion and privacy preservation, FCL has the potential to revolutionize various domains, including healthcare, finance, and industrial automation.