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Related Experiment Videos

Privacy-preserving Online Federated Learning for Massive Infinite Streams.

Liang Shi, Xuebin Ren, Shusen Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 27, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    Online federated learning (OFL) enhances privacy for decentralized data streams. This study introduces a novel sampling framework with differential privacy, improving utility and efficiency for infinite data streams.

    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Privacy

    Background:

    • Online federated learning (OFL) is crucial for privacy-preserving analytics on decentralized data streams.
    • OFL faces unique challenges like longitudinal privacy leakage and accumulated costs due to infinite data streams, unlike batch-based FL.

    Purpose of the Study:

    • To extend differential privacy (DP) to OFL for window-based privacy protection on infinite streams.
    • To propose a generic sampling-based framework for DP-enhanced OFL algorithms.
    • To develop an adaptive sampling strategy for improved utility and efficiency in OFL.

    Main Methods:

    • Extended traditional differential privacy (DP) to OFL with tunable granularity for infinite streams.
    • Developed a generic sampling-based solution framework for DP-enhanced OFL.

    Related Experiment Videos

  • Introduced Sampling³-OFL, an adaptive triple-sampling strategy using deep reinforcement learning.

    • Main Results:

    • The sampling solution framework achieves asymptotic optimality under DP constraints for infinite time horizons.
    • Sampling³-OFL dynamically determines near-optimal sampling strategies.
    • Experiments show Sampling³-OFL scales to millions of streams, improving utility by 0.74%-15.84% and reducing communication costs by 33.33%-95.24%.

    Conclusions:

    • The proposed DP-enhanced OFL framework with adaptive sampling offers significant improvements in utility and efficiency.
    • Sampling³-OFL provides a scalable and effective solution for privacy-preserving online collaborative analytics.
    • The adaptive triple-sampling strategy demonstrates the potential of deep reinforcement learning in optimizing OFL performance.