Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer
arXiv:2601.22274v2 Announce Type: replace Abstract: Real-world federated systems seldom operate on static data: input distributions drift while privacy rules forbid raw-data sharing. We study this setting as Federated Domain-Incremental Learning (FDIL), where (i) clients are heterogeneous, (ii) tasks arrive sequentially with shifting domains, yet (iii) the label space remains fixed. Two theoretical pillars remain missing for FDIL under realistic deployment: a guarantee of backward knowledge transfer (BKT) and a convergence rate that holds across the sequence of all tasks with partial participa
This paper addresses critical theoretical challenges in Federated Domain-Incremental Learning, a complex area of AI development where practical deployment faces significant hurdles.
Improving the robustness and efficiency of Federated Domain-Incremental Learning is crucial for deploying AI systems in dynamic, privacy-sensitive real-world environments.
Enhanced theoretical understanding and guarantees for FDIL could unlock new applications and accelerate the development of more adaptable and secure federated AI models.
- · AI developers
- · Cloud service providers
- · Industries with sensitive data (e.g., healthcare, finance)
- · Legacy centralized machine learning approaches
- · Systems unable to adapt to data drift
Improved federated learning algorithms that can handle evolving data distributions.
Accelerated adoption of federated learning in various sectors leading to more privacy-preserving AI applications.
A foundational shift in how AI models are continuously updated and adapted in decentralized, real-world settings without compromising data privacy.
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