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arXiv Machine Learning By Gengcheng Lyu, Manuel R\"oder, Frank-Michael Schleif

Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization

Read the original on arXiv Machine Learning →

The paper introduces Smooth‑L1 regularized Echo State Networks (SL1ESN) that encourage sparsity in the readout layer while remaining differentiable for gradient‑based training. Using a twice‑differentiable Smooth‑L1 approximation, SL1ESN is trained centrally and via a second‑order federated aggregation scheme, achieving 75–95% sparsity across seven time‑series classification benchmarks. The resulting sparse models reduce storage, inference cost, and communication overhead, with only minor accuracy loss compared to dense baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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