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.
Cross-device
- Apple: Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications |
PDF
,PDF
- Google: Towards Federated Learning at Scale: System Design |
MLSys21
,Github
🎓 - Meta: Papaya: Practical, Private, and Scalable Federated Learning |
MLSys22
🎓 - Microsoft: FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations |
PDF
,Github
- Alibaba-1: FederatedScope: A Flexible Federated Learning Platform for Heterogeneity|
PDF
- Alibaba-2: FederatedScope: FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning |
KDD22
🎓
Federated Analytics
- LinkedIn: LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale |
PDF
- Alibaba-3: Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning |
PDF
,Github
🎓
Cross-silo
- IBM: IBM Federated Learning: An Enterprise Framework White Paper |
PDF
,Github
- Nvidia: Federated Learning for Healthcare Using NVIDIA Clara |
PDF
,Github
- WeBank: Federated Learning White Paper V1.0 |
PDF
,FATE
,KubeFATE
, FATE-FLOW, FATE-LLM
- Cisco: Flame |
Github
- OpenMined: PySyft |
Github
- Baidu: Paddle |
Github
- ByteDance: Fedlearner |
Github
- Meta: FLSim |
Github
- Ant: SecretFlow |
Github
- ZTE: Neursaf FL |
Github
- FedScale: Benchmarking Model and System Performance of Federated Learning | ICML 22 🎓
- EasyFL: A Low-code Federated Learning Platform For Dummies
- Flower: A Friendly Federated Learning Research Framework
- Sherpa: Federated Learning and Differential Privacy Framework: Protect user privacy without renouncing the power of Artificial Intelligence
- FedML: A Research Library and Benchmark for Federated Machine Learning
- LEAF: A Benchmark for Federated Settings | NeurIPS 19 🎓
- FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning
- OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
- FEDn: A scalable, resilient and model agnostic hierarchical federated learning framework. - Paper
- Rosetta: A Privacy-Preserving Framework Based on TensorFlow
- FedLab: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.
Figure 1: Framework Functionality Support
- FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
- Google: TFlite |
Github
,Github
- Alibaba: MNN |
Github
- MIT: Tiny Training Engine |
Github
- Private Compute Core Architecture
- Enabling conversational interaction on mobile with LLMs | Google
Blog
- Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning | OSDI 22 🎓
- Device-centric Federated Analytics At Ease
- λ-FL : Serverless Aggregation For Federated Learning | AAAI 22 🎓
- Characterizing and Optimizing End-to-End Systems for Private Inference | ASPLOS 23 🎓
- STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining | ASPLOS 23 🎓
- Oort: Efficient Federated Learning via Guided Participant Selection | OSDI 21 🎓
- Mistify: Automating DNN Model Porting for On-Device Inference at the Edge | NSDI 21 🎓
- Pisces: Efficient Federated Learning via Guided Asynchronous Training | SoCC 22 🎓
- Resource-Efficient Federated Learning | EuroSys 23 🎓
- Federated Learning with Buffered Asynchronous Aggregation | AISTATS 22 🎓
- Hermes: An Efficient Federated Learning Framework for Heterogeneous Mobile Clients | MobiCom 21 🎓
- PyramidFL: A Fine-grained Client Selection Framework for efficient Federated Learning | MobiCom 22 🎓
- Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning | Google
- Auxo: Heterogeneity-Mitigating Federated Learning via Scalable Client Clustering | SoCC' 23
- Pisces: efficient federated learning via guided asynchronous training| SoCC' 22
- GlueFL: 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 | CVPR 20 workshop 🎓
- HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | ICLR 21 🎓
- FedorAS: Federated Architecture Search under system heterogeneity | Samsung
- Venn: Resource Management Across Federated Learning Jobs | Umich
- Green Federated Learning | Meta
- A first look into the carbon footprint of federated learning | Flower
Security
- SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost
PDF
- Secure Federated Learning for Neuroimaging
PDF
incoming
Privacy
- The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
PDF
- Differential Privacy reading list |
Github
incoming
- Google keyboard query suggestions
PDF
(2018) - Google mobile keyboard prediction
PDF
- Google Out-Of-Vocabulary Words
PDF
- Google Emoji Prediction in a Mobile Keyboard
PDF
- Google Training Speech Recognition Models (2021)
PDF
- Google Federated Learning of Gboard Language Models with Differential Privacy
PDF
- Advancing health research with Google Health Studies (2020)
Website
- Federated Evaluation of On-device Personalization
PDF
- Mobile AI benchmark
Website
- Mobile Access Bandwidth in Practice: Measurement, Analysis, and Implications
Website
- Real-world data partition FL dataset | FedScale
Website
- Mobile availability (client behavior) trace | Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data.
Website
- UNIFED: A Benchmark for Federated Learning Frameworks
- Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
- A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
- A Field Guide to Federated Optimization
- Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data
- Advances and Open Problems in Federated Learning
- On Large-Cohort Training for Federated Learning | NeurIPS 2021 🎓
- What Do We Mean by Generalization in Federated Learning? | ICLR 2022 🎓
- 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