mlsanityData to 13 Oct 2026

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC

arXiv 2509.19396 ↗First posted 23 Sep 2025cs.LG cs.AI cs.CRcited by 1

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Abstract (arXiv)

Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework designed around decoupling and clear separation of concerns for configuration, orchestration, communication, and training logic. Its architecture supports configuration-driven prototyping and code-level override-what-you-need customization. We also support different topologies, mixed communication protocols within a single deployment, and popular training algorithms. It also offers optional privacy mechanisms including Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Aggregation (SA), as well as compression strategies. These capabilities are exposed through well-defined extension points, allowing users to customize topology and orchestration, learning logic, and privacy/compression plugins, all while preserving the integrity of the core system. We evaluate multiple models and algorithms to measure various performance metrics. By unifying topology configuration, mixed-protocol communication, and pluggable modules in one stack, OmniFed streamlines FL deployment across heterogeneous environments. Github repository is available at https://github.com/at-aaims/OmniFed.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 35 of its 79 references that are in the map. First posted on arXiv 23 Sep 2025.

Builds on

35 of 79 references.

References · 35

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,981
  4. 2016Deep Learning with Differential Privacycited by 2,718
  5. 2019Searching for MobileNetV3cited by 1,990
  6. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 903
  7. 2019Towards Federated Learning at Scale: System Designcited by 830
  8. 2021Model-Contrastive Federated Learningcited by 766
  9. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 668
  10. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 570
  11. 2021Federated Learning Based on Dynamic Regularizationcited by 559
  12. 2019Federated Learning with Personalization Layerscited by 539
23 more
  1. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 518
  2. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  3. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 453
  4. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  5. 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 277
  6. 2019PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimizationcited by 237
  7. 2023DiLoCo: Distributed Low-Communication Training of Language Modelscited by 118
  8. 2024TorchTitan: One-stop PyTorch native solution for production ready LLM pre-trainingcited by 83
  9. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 79
  10. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  11. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  12. 2019Understanding Top-k Sparsification in Distributed Deep Learningcited by 56
  13. 2021TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryptioncited by 52
  14. 2020funcX: A Federated Function Serving Fabric for Sciencecited by 32
  15. 2021An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systemscited by 32
  16. 2020Faster On-Device Training Using New Federated Momentum Algorithmcited by 25
  17. 2023Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batchingcited by 12
  18. 2023GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Trainingcited by 10
  19. 2023ScaDLES: Scalable Deep Learning over Streaming data at the Edgecited by 8
  20. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  21. 2025OmniLearn: A Framework for Distributed Deep Learning over Heterogeneous Clusterscited by 5
  22. 2018RedSync : Reducing Synchronization Traffic for Distributed Deep Learningcited by 2
  23. 2025Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resourcescited by 2

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Trainingcited by 0