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Gossiped and Quantized Online Multi-Kernel Learning
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DOI：10.1109/LSP.2023.3268988.png)
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Abstract
En 中文
In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi-kernel learning provides sub-linear regret as long as every pair of nodes in the network can communicate (i.e., the communications network is a complete graph). In addition, to manage the communication load, which is often a performance bottleneck, communications between nodes can be quantized. This letter expands on these results to non-fully connected graphs, which is often the case in wireless sensor networks. To address this challenge, we propose a gossip algorithm and provide a proof that it achieves sub-linear regret. Experiments with real datasets confirm our findings.
Keywords:
Kernel
Signal processing algorithms
Radio frequency
Quantization (signal)
Mathematical models
Task analysis
Hilbert space
Federated learning
gossip algorithms
kernel-based learning
quantization
sensor networks
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
