Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data
arXiv:2606.11272v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity. In real-world settings-such as healthcare, industrial IoT (IIOT), cybersecurity, and smart cities-data streams are inherently non-stationary, leading classical FL methods to suffer from performance degradation, instability, and catastrophic forgetting. Continual Learning (CL) addresses learning under evolving data distributions but has been largely studied in centralized
The proliferation of distributed data sources and growing privacy concerns are accelerating research into methods that allow collaborative AI training without centralizing sensitive information.
This development addresses critical challenges in applying AI models to real-world, dynamic data across an increasing number of privacy-sensitive domains, unlocking new applications and improving existing ones.
The ability to perform lifelong AI learning on decentralized, non-stationary data fundamentally alters the requirements for AI deployment in sectors like healthcare and industrial IoT.
- · Healthcare providers
- · Smart city developers
- · Cybersecurity firms
- · Edge AI providers
- · Centralized data platforms
- · Traditional machine learning models lacking adaptability
- · Organizations with siloed data infrastructure
Improved AI model performance and robustness in real-world, dynamic environments due to continuous learning and privacy preservation.
Increased adoption of AI in industries with strict data governance requirements, fostering innovation in previously restricted areas.
The development of a new class of AI applications that are inherently distributed, adaptive, and privacy-centric, potentially leading to new economic models for data sharing.
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Read at arXiv cs.LG