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  • License
    Apache License 2.0
  • Created about 4 years ago
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Repository Details

Trustworthy Federated Learning Research

This repository contains research works and projects on trustworthy federated learning. It includes:

  1. Datasets. Preprocessing codes of datasets we used and developed for federated learning research.
  2. Publications. Implementation codes of our publications.
  3. Projects. Other projects in federated learning.

Federated Learning Portal

This Federated Learning Portal keeps track of books, workshops, conference special tracks, journal special issues, standardization effort and other notable events related to the field of Federated Learning (FL).

Datasets

Dataset Description
Street Dataset A real-world object detection dataset that annotates images captured by a set of street cameras based on object present in them, including 7 object categories.
Fed_ModelNet40 It consists of images taken from various views of 3D models, and can be used for vertical federated learning research.
NUS-WIDE To simulate a vertical federated learning setting, the image features of samples is put on one party and the textual tags on another party.
CheXpert CheXpert is a large dataset of chest X-rays and can be used for vertical federated learning research.

Publications

Our publications are categorized as below:

  • Highlight. Papers that have high impact or we recommend to read.
  • Security and Privacy. Security and privacy attacks and defenses.
  • Intellectual Property Protection. Intellectual property protection and ownership verification (on model or data).
  • Effectiveness. Various algorithms that aim to improve the effectiveness of FL.
  • Efficiency. Communication and computation efficiency.
  • Incentive. Incentive Mechanism.
  • Theory. Theoretical work of federated learning.
  • Application. Federated learning in real-world applications.
  • Dataset. Datasets for federated learning research.
  • Survey. Survey on various topics of federated learning.

High Citation Papers

Title Code Description Semantic Scholar Citation Google Scholar Citation (by 01/10/2023)
Federated machine learning: Concept and applications ACM TIST 2019, the 3rd most cited federated learning paper citation 2995
Advances and Open Problems in Federated Learning Foundations and Trends in Machine Learning 2021 citation 2711
SecureBoost: A Lossless Federated Learning Framework code IEEE intelligent Systems 2021, widely-used federated tree-boosting algorithm, best paper award citation 333
A Secure Federated Transfer Learning Framework code IEEE intelligent Systems 2020, the first federated transfer learning paper citation 338
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning code AAAI 2020, Innovative Application of Artificial Intelligence Award from AAAI in 2020 citation 144
Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning code 2020 USENIX ATC 2020 citation 261
A Fairness-aware Incentive Scheme for Federated Learning AIES 2020 citation 117
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attack code NIPS 2019 citation 118
Towards Personalized Federated Learning IEEE Transactions on Neural Networks and Learning Systems 2022 citation 115

Highlight Paper

Title Code Description
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning Preprint
Probably Approximately Correct Federated Learning Preprint
Trading Off Privacy, Utility and Efficiency in Federated Learning ACM TIST 2023
No Free Lunch Theorem for Security and Utility in Federated Learning ACM TIST 2022
FedIPR: Ownership Verification for Federated Deep Neural Network Models code IEEE Transactions on Pattern Analysis and Machine Intelligence 2022
SecureBoost: A Lossless Federated Learning Framework code IEEE intelligent Systems 2021, widely-used federated tree-boosting algorithm
A Secure Federated Transfer Learning Framework code IEEE intelligent Systems 2020, the first federated transfer learning paper
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning code AAAI 2020, Innovative Application of Artificial Intelligence Award from AAAI in 2020
Federated machine learning: Concept and applications ACM TIST 2019, the 3rd most cited federated learning paper

Security and Privacy

Title Code Description
Achieving Provable Byzantine Fault-Tolerance in a Semi-honest Federated Learning Setting PAKDD 2023
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation IJCAI 2023
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning preprint
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning code IJCAI 2022
Defending Batch-Level Label Inference and Replacement Attacks in Vertical Federated Learning code IEEE Transactions on Big Data
Secure Federated Matrix Factorization IEEE Intelligent Systems 2020
Privacy-Preserving Deep Learning with SPDZ The AAAI Workshop on PPAI
Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning USENIX 2020 ATC
Privacy Threats Against Federated Matrix Factorization IJCAI 2020 FL workshop
Dynamic backdoor attacks against federated learning
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks Springer Book 2020
Abnormal client behavior detection in federated learning NIPS workshop 2019

Intellectual Property Protection

Title Code Description
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model preprint
FedIPR: Ownership Verification for Federated Deep Neural Network Models IEEE TPAMI, 2022
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack CVPR 2021
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attack code NIPS 2019

Effectiveness

Title Code Description
FedHSSL: A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning code Preprint
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data ICML FL workshop 2020
A Secure Federated Transfer Learning Framework code IEEE intelligent Systems 2020
FedCVT: Semi-supervised Vertical Federated Learning with Cross-View Training ACM TIST 2022
Federated Transfer Reinforcement Learning for Autonomous Driving code Federated and Transfer Learning Book
Privacy-preserving Heterogeneous Federated Transfer Learning IEEE BigData 2019
SecureBoost: A Lossless Federated Learning Framework code IEEE intelligent Systems 2021
Multi-Component Transfer Metric Learning for handling unrelated source domain samples Knowledge-Based Systems
Cross-silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems code Federated and Transfer Learning Book

Efficiency

Title Code Description
FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning Arxiv 2021
Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning code 2020 USENIX ATC 2020
FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features code IEEE Transactions on Signal Processing 2022
RPN: A Residual Pooling Network for Efficient Federated Learning ECAI 2020
Secure and Efficient Federated Transfer Learning IEEE BigData 2019

Incentive

Title Code Description
Contribution-Aware Federated Learning for Smart Healthcare IAAI 2022
A Fairness-aware Incentive Scheme for Federated Learning AIES 2020
A Sustainable Incentive Scheme for Federated Learning IEEE Intelligent Systems
A multi-player game for studying federated learning incentive schemes IJCAI 2020

Theory

Title Code Description
Probably Approximately Correct Federated Learning Preprint
A Game-theoretic Framework for Federated Learning Preprint
Trading Off Privacy, Utility and Efficiency in Federated Learning ACM TIST 2023
No Free Lunch Theorem for Security and Utility in Federated Learning ACM TIST 2022

Application

Title Code Description
Amalur: Data Integration Meets Machine Learning ICDE 2023 (Vision paper)
StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing ACM TIST 2021
Variation-Aware Federated Learning with Multi-Source Decentralized Medical Image Data IEEE Journal of Biomedical and Health Informatics 2020
Fedml: A research library and benchmark for federated machine learning code NeurIPS 2020 FL workshop
Federated Transfer Learning for EEG Signal Classification code IEEE EMBC 2020
Multi-Agent Visualization for Explaining Federated Learning IJCAI 2019
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography IJCAI FL workshop 2020
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning code AAAI 2020
Fair and Explainable Dynamic Engagement of Crowd Workers IJCAI 2019
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention IJCAI 2020 FL workshop

Dataset

Title Code Description
Real-World Image Datasets for Federated Learning code NIPS FL workshop 2019

Survey

Title Code Description
Vertical Federated Learning Preprint
A Survey on Heterogeneous Federated Learning Preprint
Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing ACM TIST 2022
Towards Personalized Federated Learning IEEE Transactions on Neural Networks and Learning Systems
Advances and Open Problems in Federated Learning Foundations and Trends in Machine Learning 2021
Threats to Federated Learning: A Survey IJCAI FL workshop 2020
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective ArXiv 2020
Federated machine learning: Concept and applications ACM TIST 2019

Projects

Currently, we are actively contributing to two projects, FedML (research-origented) and FATE (application-oriented).

FedML

FedML (Federated Machine Learning) is a research-oriented Federated Learning Library. It provides a plenty of out-of-the-box modules in federated learning, which greatly facilitates the development of new federated learning algorithms for researchers. We are co-contributor to this project and mainly maintain the part of vertical federated learning.

FATE

FATE (Federated AI Technology Enabler) is an industrial grade Federated Learning framework. It has already incorporated many of our proposed methods and algorithms to enhance its security and efficiency under various federated learning scenarios. Some of the implemented algorithms are listed below:

License

Apache 2.0 license.