Federated Continuous Learning With Broad Network Architecture.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Federated learning (FL) enables collaborative model training while preserving data privacy by keeping raw data local.
• Existing FL models typically focus on one-time training, lacking adaptability to evolving data streams.
• Continuous learning is crucial for real-time performance and adapting to environmental changes.

Purpose of the Study:

• To introduce a novel federated continuous learning scheme (FCL-BL) that is both efficient and accurate.
• To address the limitations of existing FL models in handling continuous data streams.
• To improve the real-time performance and adaptability of federated learning systems.

Main Methods:

• A weighted processing strategy is proposed to mitigate catastrophic forgetting in continuous learning.
• A local-independent training solution is developed for fast and accurate training, avoiding synchronous or purely asynchronous methods.
• A batch-asynchronous approach and broad learning (BL) technique are employed to enhance efficiency and enable incremental learning.

Main Results:

• FCL-BL effectively handles continuous learning by addressing catastrophic forgetting.
• The local-independent training solution ensures fast and accurate model updates.
• The batch-asynchronous approach and BL technique significantly improve the efficiency of federated continuous learning.
• Experimental results demonstrate FCL-BL's superiority over existing FL schemes in efficiency and accuracy.

Conclusions:

• FCL-BL provides an efficient and accurate solution for federated continuous learning.
• The proposed methods effectively tackle challenges like catastrophic forgetting and training speed in FL.
• FCL-BL offers a robust framework for real-time adaptation in dynamic data environments.