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List: FLsystem-paper
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Federated Learning Systems Paper List
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URL: https://github.com/AmberLJC/FLsystem-paper
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Created: 2022-04-25T23:32:40.000Z (over 2 years ago)
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ultimate-awesome - FLsystem-paper - Federated Learning Systems Paper List. (Other Lists / PowerShell Lists)
README

        
# Awesome Federated Computation Systems Papers

A curated list of **FL system**-related academic papers, articles, tutorials, slides and projects. 

Star this repository, and then you can keep abreast of the latest developments of this booming research field. 

Papers with 🎓 have been peer-reviewed and presented in academic conferences.

## Table of Contents

- [Awesome Federated Computation Systems Papers](#awesome-federated-computation-systems-papers)

  - [Table of Contents](#table-of-contents)

  - [FL Systems from big tech companies](#fl-systems-from-big-tech-companies)

    - [Paper](#paper)

    - [Framework](#framework)

    - [Vertical FL](#vertical-fl)

  - [Open-source FL Framework](#open-source-fl-framework)

  - [Edge / Mobile](#edge--mobile)

  - [Federated Computation Systems](#federated-computation-systems)

  - [Optimization for FL Systems](#optimization-for-fl-systems)

  - [Security and Privacy](#security-and-privacy)

  - [Real-world FL Application](#real-world-fl-application)

  - [Real-world device traces](#real-world-device-traces)

  - [Survey](#survey)

  - [General insight for FL](#general-insight-for-fl)

  - [Other FL paper list](#other-fl-paper-list)

  

## FL Systems from big tech companies

### Paper

>Cross-device

- **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)

- **Google**: Towards Federated Learning at Scale: System Design | [`MLSys21`](https://arxiv.org/abs/1902.01046), [`Github`](https://www.tensorflow.org/federated)🎓

- **Meta**: Papaya: Practical, Private, and Scalable Federated Learning | [`MLSys22`](https://arxiv.org/abs/2111.04877) 🎓

- **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)

- **Alibaba-1**:  FederatedScope: A Flexible Federated Learning Platform for Heterogeneity| [`PDF`](https://arxiv.org/pdf/2204.05011.pdf)

- **Alibaba-2**:  FederatedScope: FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning |[`KDD22`](https://arxiv.org/abs/2204.05562) 🎓

> Federated Analytics

- **LinkedIn**: LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale | 

[`PDF`](https://arxiv.org/abs/2002.05839)

- **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) 🎓

>Cross-silo

- **IBM**: IBM Federated Learning: An Enterprise Framework White Paper | [`PDF`](https://arxiv.org/pdf/2007.10987.pdf), [`Github`](https://ibmfl.mybluemix.net/github)

- **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)

- **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)

### Framework

- Cisco: Flame | [`Github`](https://github.com/cisco-open/flame)  

  - [Federated Learning Operations Made Simple with Flame](https://arxiv.org/abs/2305.05118)

- OpenMined: PySyft | [`Github`](https://github.com/OpenMined/PySyft)

- Baidu: Paddle | [`Github`](https://github.com/PaddlePaddle/PaddleFL)

- ByteDance: Fedlearner | [`Github`](https://github.com/bytedance/fedlearner)

- Meta: FLSim | [`Github`](https://github.com/facebookresearch/FLSim)

- Ant: SecretFlow | [`Github`](https://github.com/secretflow/secretflow)

- ZTE: Neursaf FL | [`Github`](https://github.com/neursafe/federated-learning)

### Vertical FL

- OpenMined: PyVertical [`Github`](https://github.com/OpenMined/PyVertical), [`PDF`](https://arxiv.org/pdf/2104.00489.pdf)

## Open-source FL Framework

- [**FedScale**](https://github.com/SymbioticLab/FedScale): Benchmarking Model and System Performance of Federated Learning | [ICML 22](https://arxiv.org/abs/2105.11367) 🎓

- [EasyFL](https://github.com/EasyFL-AI/EasyFL): A Low-code Federated Learning Platform For Dummies

- [Flower](https://flower.dev/): A Friendly Federated Learning Research Framework

- [Sherpa](https://developers.sherpa.ai/privacy-technology/): Federated Learning and Differential Privacy Framework: Protect user privacy without renouncing the power of Artificial Intelligence

- [FedML](https://fedml.ai/): A Research Library and Benchmark for Federated Machine Learning  

- [LEAF](https://github.com/TalwalkarLab/leaf): A Benchmark for Federated Settings | [NeurIPS 19](https://arxiv.org/pdf/1812.01097.pdf) 🎓

- [FedEval](https://github.com/Di-Chai/FedEval): A Benchmark System with a Comprehensive Evaluation Model for Federated Learning

- [OpenFed](https://openfed.readthedocs.io/README.html): A Comprehensive and Versatile Open-Source Federated Learning Framework

- [FEDn](https://github.com/scaleoutsystems/fedn): A scalable, resilient and model agnostic hierarchical federated learning framework. - [Paper](https://arxiv.org/abs/2103.00148)

- [Rosetta](https://github.com/LatticeX-Foundation/Rosetta): A Privacy-Preserving Framework Based on TensorFlow

- [FedLab](https://github.com/SMILELab-FL/FedLab): A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.







[Figure 1: Framework Functionality Support](https://unifedbenchmark.github.io/leaderboard/index.html)

## FL x LLM

- [FederatedScope-LLM](https://arxiv.org/abs/2309.00363): A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

## Edge / Mobile 

- Google: TFlite | [`Github`](https://www.tensorflow.org/lite/examples/on_device_training/overview), [`Github`](https://github.com/google/federated-compute)

- Alibaba: MNN | [`Github`](https://github.com/alibaba/MNN)

- MIT: Tiny Training Engine | [`Github`](https://tinytraining.mit.edu/)

- [Private Compute Core Architecture](https://arxiv.org/pdf/2209.10317.pdf)

- Enabling conversational interaction on mobile with LLMs | Google [`Blog`](https://ai.googleblog.com/2023/05/enabling-conversational-interaction-on.html?m=1)

## Federated Computation Systems 

- [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 🎓

- [Device-centric Federated Analytics At Ease](https://arxiv.org/pdf/2206.11491.pdf)

- [λ-FL : Serverless Aggregation For Federated Learning](https://federated-learning.org/fl-aaai-2022/Papers/FL-AAAI-22_paper_44.pdf) | AAAI 22 🎓

- [Characterizing and Optimizing End-to-End Systems for Private Inference](https://arxiv.org/abs/2207.07177) | ASPLOS 23 🎓

- [STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining](https://arxiv.org/abs/2207.05022) | ASPLOS 23 🎓

## Optimization for FL Systems

- [Oort: Efficient Federated Learning via Guided Participant Selection](https://www.usenix.org/conference/osdi21/presentation/lai) | OSDI 21 🎓

- [Mistify: Automating DNN Model Porting for On-Device Inference at the Edge](https://www.usenix.org/conference/nsdi21/presentation/guo) | NSDI 21 🎓

- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://dl.acm.org/doi/abs/10.1145/3542929.3563463) | SoCC 22 🎓

- [Resource-Efficient Federated Learning](https://arxiv.org/abs/2111.01108) | EuroSys 23 🎓

- [Federated Learning with Buffered Asynchronous Aggregation](https://arxiv.org/abs/2106.06639?fbclid=IwAR2MqphvE6-_caw-MuXcjgHnFSXhJFdtbmqSEW92m-v_xeZCXXMJuqYx6Hs) | AISTATS 22 🎓

- [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 🎓

- [PyramidFL: A Fine-grained Client Selection Framework for efficient Federated Learning](https://cse.msu.edu/~caozc/papers/mobicom22-li.pdf) | MobiCom 22 🎓

- [Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning](https://arxiv.org/abs/2208.09432) | Google

- [Auxo](https://arxiv.org/abs/2210.16656): Heterogeneity-Mitigating Federated Learning via Scalable Client Clustering | SoCC' 23

- [Pisces](https://dl.acm.org/doi/abs/10.1145/3542929.3563463): efficient federated learning via guided asynchronous training| SoCC' 22

- [GlueFL](https://arxiv.org/pdf/2212.01523.pdf): Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning | MLSys' 23

- [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 🎓

- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://arxiv.org/abs/2010.01264) | ICLR 21 🎓

- [FedorAS: Federated Architecture Search under system heterogeneity](https://arxiv.org/abs/2206.11239) | Samsung

- [Venn](https://arxiv.org/abs/2312.08298): Resource Management Across Federated Learning Jobs | Umich

### Energy-efficiency

- [Green Federated Learning](https://arxiv.org/pdf/2303.14604.pdf) | Meta

- [A first look into the carbon footprint of federated learning](https://arxiv.org/abs/2102.07627) | Flower

## Security and Privacy

> Security

- SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost	[`PDF`](https://www.usenix.org/conference/usenixsecurity22/presentation/chandran)

- Secure Federated Learning for Neuroimaging [`PDF`](https://arxiv.org/pdf/2205.05249.pdf)

*incoming*

> Privacy

- The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation [`PDF`](https://arxiv.org/pdf/2102.06387.pdf)

- Differential Privacy reading list | [`Github`](https://github.com/JeffffFu/Awesome-Differential-Privacy-and-Meachine-Learning)

*incoming*

## Real-world FL Application

- Google keyboard query suggestions [`PDF`](https://arxiv.org/pdf/1812.02903.pdf) (2018)

- Google mobile keyboard prediction [`PDF`](https://arxiv.org/pdf/1811.03604.pdf)

- Google Out-Of-Vocabulary Words [`PDF`](https://arxiv.org/pdf/1903.10635.pdf)

- Google Emoji Prediction in a Mobile Keyboard [`PDF`](https://arxiv.org/pdf/1906.04329.pdf)

- Google Training Speech Recognition Models (2021) [`PDF`](https://arxiv.org/abs/2010.15965)

- Google Federated Learning of Gboard Language Models with Differential Privacy [`PDF`](https://arxiv.org/pdf/2305.18465.pdf)

- Advancing health research with Google Health Studies (2020) [`Website`](https://blog.google/technology/health/google-health-studies-app/)

- Federated Evaluation of On-device Personalization [`PDF`](https://arxiv.org/pdf/1910.10252.pdf)

## Real-world device traces

- Mobile AI benchmark [`Website`](https://ai-benchmark.com/ranking_detailed.html)

- Mobile Access Bandwidth in Practice: Measurement, Analysis, and Implications [`Website`](https://mobilebandwidth.github.io/#data-release)

- Real-world data partition FL dataset | FedScale [`Website`](https://fedscale.ai/docs/dataset)

- Mobile availability (client behavior) trace | Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data. [`Website`](https://github.com/PKU-Chengxu/FLASH)

## Survey 

- [**UNIFED: A Benchmark for Federated Learning Frameworks**](https://unifedbenchmark.github.io/)

- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://arxiv.org/abs/2109.03999)

- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://arxiv.org/pdf/1907.09693.pdf)

- [A Field Guide to Federated Optimization](https://arxiv.org/abs/2107.06917)

- [Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data](https://arxiv.org/pdf/2006.06983.pdf)

- [Advances and Open Problems in Federated Learning](https://arxiv.org/pdf/1912.04977.pdf)

## General insight for FL

- [On Large-Cohort Training for Federated Learning](https://openreview.net/forum?id=Kb26p7chwhf) | NeurIPS 2021 🎓

- [What Do We Mean by Generalization in Federated Learning?](https://openreview.net/forum?id=VimqQq-i_Q) | ICLR 2022 🎓

## Other FL paper list

- https://github.com/innovation-cat/Awesome-Federated-Machine-Learning

- https://github.com/chaoyanghe/Awesome-Federated-Learning

- https://github.com/weimingwill/awesome-federated-learning#resource-allocation

- https://github.com/youngfish42/Awesome-Federated-Learning-on-Graph-and-Tabular-Data#federated-learning-framework