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Detailed comparison of communication efficiency of split learning and federated learning

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arxiv 1909.09145 v1 pith:MYERHULY submitted 2019-09-18 cs.LG cs.DCstat.ML

Abhishek Singh , Praneeth Vepakomma , Otkrist Gupta , Ramesh Raskar This is my paper · ORCID
classification cs.LGcs.DCstat.ML
keywords learningcommunicationfederatednumberclientsincreasinglargemodels
verification ladder T0 review T1 audit T2 compute T3 formal
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We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning. We show useful settings under which each method outperforms the other in terms of communication efficiency. We consider various practical scenarios of distributed learning setup and juxtapose the two methods under various real-life scenarios. We consider settings of small and large number of clients as well as small models (1M - 6M parameters), large models (10M - 200M parameters) and very large models (1 Billion-100 Billion parameters). We show that increasing number of clients or increasing model size favors split learning setup over the federated while increasing the number of data samples while keeping the number of clients or model size low makes federated learning more communication efficient.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning

    cs.DC 2026-07 conditional novelty 6.0 of 10

    An autoencoder-based split-learning compressor with a two-stage alignment protocol achieves about 10x communication reduction during pre-trained vision-model fine-tuning with near-zero accuracy loss, outperforming heu...

  2. ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning

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    Class-prototype consistency vectors plus class-conditional conformal filtering cut backdoor ASR to ~0.03–0.08 on CIFAR-10, SVHN, and Bank Marketing while preserving main accuracy.

  3. Navigating the Edge-Cloud Continuum: A State-of-Practice Survey

    cs.DC 2025-05 conditional novelty 5.0 of 10

    A state-of-practice survey that maps the edge-cloud continuum through a developer-oriented five-area conceptual framework.

  4. Federated Split Learning with Improved Communication and Storage Efficiency

    cs.LG 2025-07 conditional novelty 4.0 of 10

    CSE-FSL combines an auxiliary network for local updates with periodic smashed-data uploads and a single server-side model, claiming convergence under non-convex loss and lower communication and storage costs.

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