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HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

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arxiv 2010.01264 v3 pith:5UUKSKRS submitted 2020-10-03 cs.LG stat.ML

Enmao Diao , Jie Ding , Vahid Tarokh This is my paper
classification cs.LGstat.ML
keywords computationclientsheterogeneouslearningcommunicationfederatedmodelmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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Federated Learning (FL) is a method of training machine learning models on private data distributed over a large number of possibly heterogeneous clients such as mobile phones and IoT devices. In this work, we propose a new federated learning framework named HeteroFL to address heterogeneous clients equipped with very different computation and communication capabilities. Our solution can enable the training of heterogeneous local models with varying computation complexities and still produce a single global inference model. For the first time, our method challenges the underlying assumption of existing work that local models have to share the same architecture as the global model. We demonstrate several strategies to enhance FL training and conduct extensive empirical evaluations, including five computation complexity levels of three model architecture on three datasets. We show that adaptively distributing subnetworks according to clients' capabilities is both computation and communication efficient.

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Cited by 5 Pith papers

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

  1. H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity

    cs.LG 2025-07 conditional novelty 6.0 of 10

    H2Tune enables federated fine-tuning across heterogeneous foundation models by sharing sparsified rank-aligned middle matrices with learned layer mappings and alternating shared/private updates.

  2. Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems

    cs.LG 2026-07 conditional novelty 5.5 of 10

    Collate jointly trains heterogeneous models under per-device latency constraints via dynamic zeroizing-recovering and proto-corrected aggregation, gaining ~2–3% accuracy over prior heterogeneous FL.

  3. Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation

    cs.CV 2025-09 conditional novelty 4.0 of 10

    Federated YOLO and Deformable DETR detectors are evaluated for CAVs on KITTI, BDD100K, and nuScenes, with resource profiling under non-IID splits, client dropout, and weather and lighting shifts.

  4. UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data

    cs.LG 2025-06 reject novelty 4.0 of 10

    UniVarFL adds a classifier variance regularizer and a hyperspherical uniformity regularizer to local federated training, reporting improved accuracy on some non-IID benchmarks but not consistently across its own experiments.

  5. Heterogeneous Federated Learning with Prototype Alignment and Upscaling

    cs.LG 2025-07 conditional novelty 3.0 of 10

    ProtoNorm adds server-side prototype alignment and a per-dataset scaling factor to FedProto-style federated learning, improving accuracy but with the gain largely driven by the tuned scaling factor.

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