{"id":14110178,"url":"https://github.com/AmberLJC/FLsystem-paper","last_synced_at":"2025-08-01T09:33:12.417Z","repository":{"id":39614121,"uuid":"485567517","full_name":"AmberLJC/FLsystem-paper","owner":"AmberLJC","description":"Federated Learning Systems Paper 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Lists","acknowledgments"],"sub_categories":["TeX Lists","secret sharing"],"readme":"\n# Awesome Federated Computation Systems Papers\n\nA curated list of **FL system**-related academic papers, articles, tutorials, slides and projects. \nStar this repository, and then you can keep abreast of the latest developments of this booming research field. \n\nPapers with 🎓 have been peer-reviewed and presented in academic conferences.\n\n\n## Table of Contents\n- [Awesome Federated Computation Systems Papers](#awesome-federated-computation-systems-papers)\n  - [Table of Contents](#table-of-contents)\n  - [FL Systems from big tech companies](#fl-systems-from-big-tech-companies)\n    - [Paper](#paper)\n    - [Framework](#framework)\n    - [Vertical FL](#vertical-fl)\n  - [Open-source FL Framework](#open-source-fl-framework)\n  - [Edge / Mobile](#edge--mobile)\n  - [Federated Computation Systems](#federated-computation-systems)\n  - [Optimization for FL Systems](#optimization-for-fl-systems)\n  - [Security and Privacy](#security-and-privacy)\n  - [Real-world FL Application](#real-world-fl-application)\n  - [Real-world device traces](#real-world-device-traces)\n  - [Survey](#survey)\n  - [General insight for FL](#general-insight-for-fl)\n  - [Other FL paper list](#other-fl-paper-list)\n  \n\n\n## FL Systems from big tech companies\n### Paper\n\n\u003eCross-device\n\n- **Apple**:  Federated Evaluation and Tuning for On-Device Personalization: System Design \u0026 Applications | [`PDF`](https://arxiv.org/pdf/2102.08503.pdf), [`PDF`](https://docs-assets.developer.apple.com/ml-research/papers/learning-with-privacy-at-scale.pdf)\n- **Google**: Towards Federated Learning at Scale: System Design | [`MLSys21`](https://arxiv.org/abs/1902.01046), [`Github`](https://www.tensorflow.org/federated)🎓\n- **Meta**: Papaya: Practical, Private, and Scalable Federated Learning | [`MLSys22`](https://arxiv.org/abs/2111.04877) 🎓\n- **Microsoft**:  FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations | [`PDF`](https://arxiv.org/abs/2203.13789), [`Github`](https://github.com/microsoft/msrflute)\n- **Alibaba-1**:  FederatedScope: A Flexible Federated Learning Platform for Heterogeneity| [`PDF`](https://arxiv.org/pdf/2204.05011.pdf)\n- **Alibaba-2**:  FederatedScope: FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning |[`KDD22`](https://arxiv.org/abs/2204.05562) 🎓\n\n\n\n\u003e Federated Analytics\n- **LinkedIn**: LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale | \n[`PDF`](https://arxiv.org/abs/2002.05839)\n- **Alibaba-3**:  Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning | [`PDF`](https://www.usenix.org/system/files/osdi22-lv.pdf), [`Github`](https://github.com/alibaba/MNN) 🎓\n\n\n\n\n\u003eCross-silo\n\n- **IBM**: IBM Federated Learning: An Enterprise Framework White Paper | [`PDF`](https://arxiv.org/pdf/2007.10987.pdf), [`Github`](https://ibmfl.mybluemix.net/github)\n- **Nvidia**:  Federated Learning for Healthcare Using NVIDIA *Clara* | [`PDF`](https://developer.download.nvidia.com/CLARA/Federated-Learning-Training-for-Healthcare-Using-NVIDIA-Clara.pdf), [`Github`](https://github.com/NVIDIA/NVFlare)\n- **WeBank**:  Federated Learning White Paper V1.0 | [`PDF`](​​https://aisp-1251170195.cos.ap-hongkong.myqcloud.com/fedweb/1552917186945.pdf),  [`FATE`](https://github.com/FederatedAI/FATE), [`KubeFATE`](https://github.com/FederatedAI/KubeFATE), [FATE-FLOW](https://federatedai.github.io/FATE-Flow/latest/fate_flow/), [FATE-LLM](https://arxiv.org/pdf/2310.10049.pdf)\n\n\n\n\n### Framework\n- Cisco: Flame | [`Github`](https://github.com/cisco-open/flame)  \n  - [Federated Learning Operations Made Simple with Flame](https://arxiv.org/abs/2305.05118)\n- OpenMined: PySyft | [`Github`](https://github.com/OpenMined/PySyft)\n- Baidu: Paddle | [`Github`](https://github.com/PaddlePaddle/PaddleFL)\n- ByteDance: Fedlearner | [`Github`](https://github.com/bytedance/fedlearner)\n- Meta: FLSim | [`Github`](https://github.com/facebookresearch/FLSim)\n- Ant: SecretFlow | [`Github`](https://github.com/secretflow/secretflow)\n- ZTE: Neursaf FL | [`Github`](https://github.com/neursafe/federated-learning)\n\n### Vertical FL\n- OpenMined: PyVertical [`Github`](https://github.com/OpenMined/PyVertical), [`PDF`](https://arxiv.org/pdf/2104.00489.pdf)\n\n## Open-source FL Framework\n- [**FedScale**](https://github.com/SymbioticLab/FedScale): Benchmarking Model and System Performance of Federated Learning | [ICML 22](https://arxiv.org/abs/2105.11367) 🎓\n- [EasyFL](https://github.com/EasyFL-AI/EasyFL): A Low-code Federated Learning Platform For Dummies\n- [Flower](https://flower.dev/): A Friendly Federated Learning Research Framework\n- [Sherpa](https://developers.sherpa.ai/privacy-technology/): Federated Learning and Differential Privacy Framework: Protect user privacy without renouncing the power of Artificial Intelligence\n- [FedML](https://fedml.ai/): A Research Library and Benchmark for Federated Machine Learning  \n- [LEAF](https://github.com/TalwalkarLab/leaf): A Benchmark for Federated Settings | [NeurIPS 19](https://arxiv.org/pdf/1812.01097.pdf) 🎓\n- [FedEval](https://github.com/Di-Chai/FedEval): A Benchmark System with a Comprehensive Evaluation Model for Federated Learning\n- [OpenFed](https://openfed.readthedocs.io/README.html): A Comprehensive and Versatile Open-Source Federated Learning Framework\n- [FEDn](https://github.com/scaleoutsystems/fedn): A scalable, resilient and model agnostic hierarchical federated learning framework. - [Paper](https://arxiv.org/abs/2103.00148)\n- [Rosetta](https://github.com/LatticeX-Foundation/Rosetta): A Privacy-Preserving Framework Based on TensorFlow\n- [FedLab](https://github.com/SMILELab-FL/FedLab): A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.\n\n\n\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"framework-summary.png\" width=\"1000\" height=\"700\"/\u003e\n\u003c/p\u003e\n\n[Figure 1: Framework Functionality Support](https://unifedbenchmark.github.io/leaderboard/index.html)\n\n## FL x LLM\n- [FederatedScope-LLM](https://arxiv.org/abs/2309.00363): A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning\n\n\n## Edge / Mobile \n- Google: TFlite | [`Github`](https://www.tensorflow.org/lite/examples/on_device_training/overview), [`Github`](https://github.com/google/federated-compute)\n- Alibaba: MNN | [`Github`](https://github.com/alibaba/MNN)\n- MIT: Tiny Training Engine | [`Github`](https://tinytraining.mit.edu/)\n- [Private Compute Core Architecture](https://arxiv.org/pdf/2209.10317.pdf)\n- Enabling conversational interaction on mobile with LLMs | Google [`Blog`](https://ai.googleblog.com/2023/05/enabling-conversational-interaction-on.html?m=1)\n\n## Federated Computation Systems \n- [Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning](https://www.usenix.org/system/files/osdi22-lv.pdf) | OSDI 22 🎓\n- [Device-centric Federated Analytics At Ease](https://arxiv.org/pdf/2206.11491.pdf)\n- [λ-FL : Serverless Aggregation For Federated Learning](https://federated-learning.org/fl-aaai-2022/Papers/FL-AAAI-22_paper_44.pdf) | AAAI 22 🎓\n- [Characterizing and Optimizing End-to-End Systems for Private Inference](https://arxiv.org/abs/2207.07177) | ASPLOS 23 🎓\n- [STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining](https://arxiv.org/abs/2207.05022) | ASPLOS 23 🎓\n\n## Optimization for FL Systems\n- [Oort: Efficient Federated Learning via Guided Participant Selection](https://www.usenix.org/conference/osdi21/presentation/lai) | OSDI 21 🎓\n- [Mistify: Automating DNN Model Porting for On-Device Inference at the Edge](https://www.usenix.org/conference/nsdi21/presentation/guo) | NSDI 21 🎓\n- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://dl.acm.org/doi/abs/10.1145/3542929.3563463) | SoCC 22 🎓\n- [Resource-Efficient Federated Learning](https://arxiv.org/abs/2111.01108) | EuroSys 23 🎓\n- [Federated Learning with Buffered Asynchronous Aggregation](https://arxiv.org/abs/2106.06639?fbclid=IwAR2MqphvE6-_caw-MuXcjgHnFSXhJFdtbmqSEW92m-v_xeZCXXMJuqYx6Hs) | AISTATS 22 🎓\n- [Hermes: An Efficient Federated Learning Framework for Heterogeneous Mobile Clients](https://sites.duke.edu/angli/files/2021/10/2021_Mobicom_Hermes_v1.pdf) | MobiCom 21 🎓\n- [PyramidFL: A Fine-grained Client Selection Framework for efficient Federated Learning](https://cse.msu.edu/~caozc/papers/mobicom22-li.pdf) | MobiCom 22 🎓\n- [Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning](https://arxiv.org/abs/2208.09432) | Google\n- [Auxo](https://arxiv.org/abs/2210.16656): Heterogeneity-Mitigating Federated Learning via Scalable Client Clustering | SoCC' 23\n- [Pisces](https://dl.acm.org/doi/abs/10.1145/3542929.3563463): efficient federated learning via guided asynchronous training| SoCC' 22\n- [GlueFL](https://arxiv.org/pdf/2212.01523.pdf): Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning | MLSys' 23\n- [Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search](https://arxiv.org/abs/2004.08546) | CVPR 20 workshop 🎓\n- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://arxiv.org/abs/2010.01264) | ICLR 21 🎓\n- [FedorAS: Federated Architecture Search under system heterogeneity](https://arxiv.org/abs/2206.11239) | Samsung\n- [Venn](https://arxiv.org/abs/2312.08298): Resource Management Across Federated Learning Jobs | Umich\n\n### Energy-efficiency\n- [Green Federated Learning](https://arxiv.org/pdf/2303.14604.pdf) | Meta\n- [A first look into the carbon footprint of federated learning](https://arxiv.org/abs/2102.07627) | Flower\n\n\n## Security and Privacy\n\u003e Security\n- SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost\t[`PDF`](https://www.usenix.org/conference/usenixsecurity22/presentation/chandran)\n- Secure Federated Learning for Neuroimaging [`PDF`](https://arxiv.org/pdf/2205.05249.pdf)\n\n*incoming*\n\n\u003e Privacy\n- The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation [`PDF`](https://arxiv.org/pdf/2102.06387.pdf)\n- Differential Privacy reading list | [`Github`](https://github.com/JeffffFu/Awesome-Differential-Privacy-and-Meachine-Learning)\n\n*incoming*\n\n## Real-world FL Application\n- Google keyboard query suggestions [`PDF`](https://arxiv.org/pdf/1812.02903.pdf) (2018)\n- Google mobile keyboard prediction [`PDF`](https://arxiv.org/pdf/1811.03604.pdf)\n- Google Out-Of-Vocabulary Words [`PDF`](https://arxiv.org/pdf/1903.10635.pdf)\n- Google Emoji Prediction in a Mobile Keyboard [`PDF`](https://arxiv.org/pdf/1906.04329.pdf)\n- Google Training Speech Recognition Models (2021) [`PDF`](https://arxiv.org/abs/2010.15965)\n- Google Federated Learning of Gboard Language Models with Differential Privacy [`PDF`](https://arxiv.org/pdf/2305.18465.pdf)\n- Advancing health research with Google Health Studies (2020) [`Website`](https://blog.google/technology/health/google-health-studies-app/)\n- Federated Evaluation of On-device Personalization [`PDF`](https://arxiv.org/pdf/1910.10252.pdf)\n\n## Real-world device traces\n- Mobile AI benchmark [`Website`](https://ai-benchmark.com/ranking_detailed.html)\n- Mobile Access Bandwidth in Practice: Measurement, Analysis, and Implications [`Website`](https://mobilebandwidth.github.io/#data-release)\n- Real-world data partition FL dataset | FedScale [`Website`](https://fedscale.ai/docs/dataset)\n- Mobile availability (client behavior) trace | Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data. [`Website`](https://github.com/PKU-Chengxu/FLASH)\n\n\n## Survey \n- [**UNIFED: A Benchmark for Federated Learning Frameworks**](https://unifedbenchmark.github.io/)\n- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://arxiv.org/abs/2109.03999)\n- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://arxiv.org/pdf/1907.09693.pdf)\n- [A Field Guide to Federated Optimization](https://arxiv.org/abs/2107.06917)\n- [Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data](https://arxiv.org/pdf/2006.06983.pdf)\n- [Advances and Open Problems in Federated Learning](https://arxiv.org/pdf/1912.04977.pdf)\n\n\n## General insight for FL\n- [On Large-Cohort Training for Federated Learning](https://openreview.net/forum?id=Kb26p7chwhf) | NeurIPS 2021 🎓\n- [What Do We Mean by Generalization in Federated Learning?](https://openreview.net/forum?id=VimqQq-i_Q) | ICLR 2022 🎓\n\n## Other FL paper list\n- https://github.com/innovation-cat/Awesome-Federated-Machine-Learning\n- https://github.com/chaoyanghe/Awesome-Federated-Learning\n- https://github.com/weimingwill/awesome-federated-learning#resource-allocation\n- https://github.com/youngfish42/Awesome-Federated-Learning-on-Graph-and-Tabular-Data#federated-learning-framework\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAmberLJC%2FFLsystem-paper","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FAmberLJC%2FFLsystem-paper","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAmberLJC%2FFLsystem-paper/lists"}