arXiv:2608. 04545v1 Announce Type: new Abstract: We study the problem of learning compact rule-based compressors for structured network traffic.
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.
arXiv:2608.21874v1 Announce Type: cross Abstract: Deep learning has achieved strong performance in encrypted traffic classification (ETC), yet its computational cost limits deployment on resource-con...
arXiv:2603.25507v2 Announce Type: replace-cross Abstract: Network Traffic Classification (NTC) increasingly relies on data-driven models, yet its practical deployment is often constrained by limited...
The paper introduces Layer-wise Budgeted Adaptive Transmission (LBAT), a method that treats federated learning communication as a resource allocation problem under tight uplink budgets. LBAT estimates the transmission value of each layer using local training signals and then applies a byte‑level dynamic programming allocator to choose optimal rank and bit configurations for each layer. Experiments on heterogeneous federated tabular prediction and data generation tasks show that LBAT outperforms uniform compression baselines, achieving better communication‑utility tradeoffs while maintaining distributional fidelity.
arXiv:2506. 01260v2 Announce Type: replace Abstract: Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks.
The paper explores how to combine Federated Learning with TinyML model compression techniques—such as knowledge distillation, structured pruning, and quantization—to create lightweight, privacy‑preserving intrusion detection systems for IoT devices. It evaluates these strategies within a federated training pipeline and finds that training stability is crucial; a server‑coordinated cosine learning‑rate schedule boosts Attack Recall from 46.7% to 93.85% while still allowing significant model compression and efficient edge deployment.
The paper discusses how any lossless compression algorithm can be transformed into a machine learning method using Normalized Compression Distance or the Minimum Description Length principle, and conversely how any auto‑regressive model can become a lossless compressor via entropy coding. It surveys and formalizes these strategies, introduces a design framework for compression‑based ML, and empirically validates that such methods can match conventional baselines and outperform them on malware detection, achieving accuracy gains up to 0.62 by varying design choices.