Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-stationary environment. To achieve high reliability and scalability in deploying this paradigm in distributed systems, it...
Gespeichert in:
Hauptverfasser: | , , , , , , , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for
enabling distributed devices such as vehicles and servers to handle streaming
data from a joint non-stationary environment. To achieve high reliability and
scalability in deploying this paradigm in distributed systems, it is essential
to conquer challenges stemming from both spatial and temporal dimensions,
manifesting as distribution shifts, catastrophic forgetting, heterogeneity, and
privacy issues. This survey focuses on a comprehensive examination of the
development of the non-centralized continual learning algorithms and the
real-world deployment across distributed devices. We begin with an introduction
to the background and fundamentals of non-centralized learning and continual
learning. Then, we review existing solutions from three levels to represent how
existing techniques alleviate the catastrophic forgetting and distribution
shift. Additionally, we delve into the various types of heterogeneity issues,
security, and privacy attributes, as well as real-world applications across
three prevalent scenarios. Furthermore, we establish a large-scale benchmark to
revisit this problem and analyze the performance of the state-of-the-art NCCL
approaches. Finally, we discuss the important challenges and future research
directions in NCCL. |
---|---|
DOI: | 10.48550/arxiv.2412.13840 |