Federated Continual Learning with Weighted Inter-client Transfer (2003.03196v5)

Abstract: 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. This problem of federated continual learning poses new challenges to continual learning, such as utilizing knowledge from other clients, while preventing interference from irrelevant knowledge. To resolve these issues, we propose a novel federated continual learning framework, Federated Weighted Inter-client Transfer (FedWeIT), which decomposes the network weights into global federated parameters and sparse task-specific parameters, and each client receives selective knowledge from other clients by taking a weighted combination of their task-specific parameters. FedWeIT minimizes interference between incompatible tasks, and also allows positive knowledge transfer across clients during learning. We validate our FedWeIT against existing federated learning and continual learning methods under varying degrees of task similarity across clients, and our model significantly outperforms them with a large reduction in the communication cost. Code is available at https://github.com/wyjeong/FedWeIT

• The paper introduces FedWeIT, a framework that decomposes model weights into global and sparse task-specific parameters to mitigate inter-client interference.
• It employs an attention-based mechanism for selective knowledge transfer, boosting learning efficiency and reducing communication overhead.
• Empirical results show improved accuracy and reduced catastrophic forgetting across diverse tasks, validated on datasets like Overlapped-CIFAR-100 and NonIID-50.

Federated Continual Learning with Weighted Inter-client Transfer: An Overview

The paper "Federated Continual Learning with Weighted Inter-client Transfer" introduces a novel framework, FedWeIT, aimed at addressing the challenges of integrating federated learning with continual learning. As deep learning models are increasingly deployed in real-world scenarios, the capabilities of these models to continuously learn from a sequence of tasks while maintaining data privacy and low communication overhead become critical. This research explores the intricate problem of federated continual learning (FCL), which requires a model operating across multiple clients, each with its sequence of private tasks, to not only learn continuously but also exchange knowledge effectively with other clients under federated learning constraints.

The FedWeIT Framework

FedWeIT stands out due to its approach to decomposing the network weights into a combination of global federated parameters and sparse task-specific parameters. This decomposition is implemented to manage the dual imperatives of knowledge sharing and retention. The global federated parameters aggregate the knowledge shared across clients, while the sparse task-specific parameters help retain individual task expertise. By leveraging a weighted transfer approach, FedWeIT allows each client to selectively incorporate relevant task-specific knowledge from other clients, thus minimizing irrelevant task interference—a common issue that affects both model performance and learning efficiency.

Core Contributions and Methodology

The paper makes several significant contributions to the domain of machine learning. First, it formulates the problem of FCL, highlighting unique challenges such as inter-client interference and the necessity for efficient inter-client knowledge transfer. Second, the introduction of FedWeIT offers a pioneering solution that incorporates both parameter decomposition and a selective transfer mechanism to facilitate efficient learning across distributed, privacy-sensitive environments.

The methodology involves implementing an attention-based mechanism to prioritize useful knowledge from the set of task-adaptive parameters pooled by the clients. This mechanism ensures that each client dynamically adjusts its model updates based on the relevance of the shared task-specific knowledge to its current learning context. This is achieved through a collaborative yet decentralized system of parameter exchange moderated by a central server, which aggregates and redistributes sparse task-adaptive parameters across the network.

Empirical Validation and Results

FedWeIT is empirically validated against standard continual learning and federated learning methods across diverse datasets, including Overlapped-CIFAR-100 and NonIID-50, which test the model's efficacy in scenarios of varying task similarity and heterogeneity across clients. The framework demonstrates clear superiority, achieving higher accuracy levels while significantly reducing communication costs. Specifically, the results showcased that FedWeIT could adapt effectively to new tasks and reduce catastrophic forgetting, a persistent challenge in continual learning. The structuring of communication to involve only sparse parameter sets allows it to maintain efficiency in data transfer, crucial for real-world deployment on resource-constrained devices.

Implications and Future Directions

This paper not only provides an effective solution to the FCL challenge but also lays the groundwork for further exploration in federated machine learning. FedWeIT's framework elevates the capability of models to leverage distributed data in a privacy-compliant manner while ensuring optimal learning performance. Future research might explore refining the weighted transfer approach to further personalize the learning experiences of clients or explore adaptive mechanisms that adjust federated parameters in real-time based on dynamic task shifts.

Conclusion

In conclusion, "Federated Continual Learning with Weighted Inter-client Transfer" represents a noteworthy advancement in the intersection of federated and continual learning domains. By enabling robust and efficient learning across diverse tasks and clients while preserving system privacy and reducing communication overhead, it sets a promising direction for the development of scalable, intelligent systems capable of continual improvement over time.