Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe

This paper presents a continual learning approach that enables models to learn from streaming data while preserving individual participant privacy through buffering and aggregation techniques. The method is designed to be auditable, allowing verification that privacy guarantees have been maintained throughout the learning process, addressing a key challenge for organizations deploying adaptive AI systems in regulated environments.

This paper presents a continual learning approach that enables models to learn from streaming data while preserving individual participant privacy through buffering and aggregation techniques. The method is designed to be auditable, allowing verification that privacy guarantees have been maintained throughout the learning process, addressing a key challenge for organizations deploying adaptive AI systems in regulated environments.

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