{"data":{"slug":"amberljc-flsystem-paper","name":"FLsystem-paper","tagline":"Federated Learning Systems Paper List","github_url":"https://github.com/AmberLJC/FLsystem-paper","owner":"AmberLJC","repo":"FLsystem-paper","owner_avatar_url":"https://avatars.githubusercontent.com/u/42296458?v=4","primary_language":null,"stars":75,"forks":7,"topics":["awesome-list","federated-learning","machine-learning","mlsys","papers","systems"],"archived":false,"github_pushed_at":"2024-02-07T05:08:39+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/amberljc-flsystem-paper","markdown_url":"https://www.graphcanon.com/tools/amberljc-flsystem-paper.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/amberljc-flsystem-paper","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=amberljc-flsystem-paper","description":"Federated Learning Systems Paper List","homepage_url":null,"license":null,"open_issues":1,"watchers":4,"ai_summary":null,"readme_excerpt":"# 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>Cross-device\n\n- **Apple**:  Federated Evaluation and Tuning for On-Device Personalization: System Design & 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> 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>Cross-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/FL","github_created_at":"2022-04-25T23:32:40+00:00","created_at":"2026-07-11T23:38:50.320707+00:00","updated_at":"2026-07-11T23:38:54.902634+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"mlsys","name":"mlsys"},{"slug":"systems","name":"systems"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"federated-learning","name":"federated-learning"},{"slug":"awesome-list","name":"awesome-list"},{"slug":"papers","name":"papers"}],"trust":{"provenance":{"is_fork":false,"github_id":485567517,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T23:38:51.352Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":885,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:38:51.667Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T23:38:51.118Z"}}}}