FedML’s core technology is backed by years of cutting-edge research represented in 50+ publications in ML/FL Algorithms, Security/Privacy, Systems, and Applications.
Vision Paper for High Scientific Impacts
System for Large-scale Distributed/Federated Training
Training Algorithms for FL
Security/privacy for FL
AI Applications A Full-stack of Scientific Publications in ML Algorithms, Security/Privacy, Systems, Applications, and Visionary Impacts
Vision Paper for High Scientific Impacts¶
Being visionary to find the correct problems is always the key to impactful research.
 Open Problems and Advances in Federated Learning. FnTML 2021.
 Field Guide for Federated Learning (Arxiv 2021)
System for Large-scale Distributed/Federated Training¶
Towards communication/computation/memory-efficient, resilient and robust distributed training and inferences via ML+system co-design and real-world implementation.
 A fundamental tradeoff between computation and communication in distributed computing (IEEE Transactions on Information Theory)
 FedML: A Research Library and Benchmark for Federated Machine Learning (NeurIPS 2020 FL Workshop, Best Paper Award)
 OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning (ICML 2021 FL Workshop)
 Communication-aware scheduling of serial tasks for dispersed computing (IEEE/ACM Transactions on Networking)
Training Algorithms for FL¶
Algorithmic innovation to land distributed training and inference on the edge into the real-world system, solving challenges in efficiency, scalability, label deficiency, personalization, fairness, low-latency, straggler mitigation, etc.
 FedNAS (neural architecture search for FL personalization) at CVPR’20 NAS Workshop
 SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision (FL-AAAI’22, Best Paper Award)
 FairFed: Enabling Group Fairness in Federated Learning (NeurIPS 2021 FL workshop)
 Accelerated Distributed Approximate Newton Method (TNNLS Journal, 2022)
 Coded Computing for Low-Latency Federated Learning Over Wireless Edge Networks (IEEE Journal on Selected Areas in Communications)
 Coded computation over heterogeneous clusters (IEEE Transactions on Information Theory)
 Hierarchical coded gradient aggregation for learning at the edge (ISIT 2020)
 Straggler mitigation in distributed matrix multiplication: Fundamental limits and optimal coding (IEEE Transactions on Information Theory)
Security/privacy for FL¶
Privacy-preserving, Attack, and Defense
 Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning (end-to-end privacy protection in FL)
 A scalable approach for privacy-preserving collaborative machine learning (NeurIPS 2020)
 Secure aggregation for buffered asynchronous federated learning (Arxiv’2021)
 CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning (IEEE/ACM Transactions on Networking)
 CodedPrivateML: A fast and privacy-preserving framework for distributed machine learning (IEEE Journal on Selected Areas in Information Theory)
 Byzantine-resilient secure federated learning (IEEE Journal on Selected Areas in Information Theory)
 Coded merkle tree: Solving data availability attacks in blockchains (International Conference on Financial Cryptography and Data Security)
 Polyshard: Coded sharding achieves linearly scaling efficiency and security simultaneously (IEEE Transactions on Information Forensics and Security)
Besides fundamental research in FL, we also target important applications in Natural Language Processing, Computer Vision, Data Mining, and the Internet of Things (IoTs).
 FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks (ICLR 2021 workshop; KDD 2021 workshop)
 FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks (FL-AAAI’2022)
 Federated Learning for Internet of Things (ACM Sensys’21)
 AutoCTS: Automated Correlated Time Series Forecasting (VLDB 2022)
 Coded computing for distributed graph analytics (IEEE Transactions on Information Theory)
 TACC: Topology-aware coded computing for distributed graph processing (IEEE Transactions on Signal and Information Processing over Networks)
 Privacy-Aware Distributed Graph-Based Semi-Supervised Learning (2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
 Lightweight Image Super-Resolution with Hierarchical and Differentiable Neural Architecture Search (IJCV Journal Under Review)