Research Publications

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.

Outline

1. Vision Paper for High Scientific Impacts
2. System for Large-scale Distributed/Federated Training
3. Training Algorithms for FL
4. Security/privacy for FL
5. 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.

[1][Open Problems and Advances in Federated Learning](https://arxiv.org/abs/1912.04977). FnTML 2021.

[2][Field Guide for Federated Learning](https://arxiv.org/abs/2107.06917) (Arxiv 2021)

[3][Federated learning for Internet of Things: : Applications, Challenges, and Opportunities](https://arxiv.org/abs/2111.07494) (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.

[1][A fundamental tradeoff between computation and communication in distributed computing](https://ieeexplore.ieee.org/abstract/document/8051074) (IEEE Transactions on Information Theory)

[2][FedML: A Research Library and Benchmark for Federated Machine Learning](https://arxiv.org/abs/2007.13518) (NeurIPS 2020 FL Workshop, Best Paper Award)

[3][PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers](http://proceedings.mlr.press/v139/he21a/he21a.pdf) (ICML 2021)

[4][Pipe-SGD: A decentralized pipelined SGD framework for distributed deep net training](https://proceedings.neurips.cc/paper/2018/file/2c6a0bae0f071cbbf0bb3d5b11d90a82-Paper.pdf) (NeurIPS 2018)

[5][Gradiveq: Vector quantization for bandwidth-efficient gradient aggregation in distributed cnn training](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=100&pagesize=100&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:NJ774b8OgUMC) (NeurIPS 2018)

[6][MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge](https://proceedings.neurips.cc/paper/2021/hash/ae3f4c649fb55c2ee3ef4d1abdb79ce5-Abstract.html) (NeurIPS 2021)

[7][ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems](https://arxiv.org/abs/2109.09868) (NeurIPS 2021)

[8][Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy](https://arxiv.org/abs/1806.00939) (AISTATS 2019)

[9][OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=2z2camUAAAAJ&sortby=pubdate&citation_for_view=2z2camUAAAAJ:_Qo2XoVZTnwC) (ICML 2021 FL Workshop)

[10][AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference](https://arxiv.org/abs/2110.01229) (Arxiv 2022)

[11][Communication-aware scheduling of serial tasks for dispersed computing](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:eq2jaN3J8jMC) (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.

[1][Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge](https://arxiv.org/abs/2007.14513) (NeurIPS’20)

[2][FedNAS (neural architecture search for FL personalization)](https://arxiv.org/abs/2004.08546) at CVPR’20 NAS Workshop

[3][SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks](https://arxiv.org/abs/2106.02743) (AAAI’21)

[4][SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision](https://arxiv.org/abs/2110.02470) (FL-AAAI’22, Best Paper Award)

[5][FairFed: Enabling Group Fairness in Federated Learning](https://arxiv.org/abs/2110.00857) (NeurIPS 2021 FL workshop)

[6][Accelerated Distributed Approximate Newton Method](https://pubmed.ncbi.nlm.nih.gov/35254992/) (TNNLS Journal, 2022)

[7][Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits](https://arxiv.org/abs/2201.03789) (FL-AAAI’2022)

[8][SPIDER: Searching Personalized Neural Architecture for Federated Learning](https://arxiv.org/abs/2112.13939) (Arxiv’ 2022)

[9][Layer-wise Adaptive Model Aggregation for Scalable Federated Learning](https://arxiv.org/abs/2110.10302) (Arxiv’2022)

[10][Achieving Small-Batch Accuracy with Large-Batch Scalability via Adaptive Learning Rate Adjustment](https://openreview.net/forum?id=39Q__qgCpAH) (Arxiv’ 2022)

[11][Coded Computing for Low-Latency Federated Learning Over Wireless Edge Networks](https://ieeexplore.ieee.org/abstract/document/9252954) (IEEE Journal on Selected Areas in Communications)

[12][Coded computation over heterogeneous clusters](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=VhnTrugAAAAJ&citation_for_view=VhnTrugAAAAJ:u-x6o8ySG0sC) (IEEE Transactions on Information Theory)

[13][Hierarchical coded gradient aggregation for learning at the edge](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:DUooU5lO8OsC) (ISIT 2020)

[14][Coded computing for federated learning at the edge](https://arxiv.org/abs/2007.03273)

[15][Straggler mitigation in distributed matrix multiplication: Fundamental limits and optimal coding](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:JoZmwDi-zQgC) (IEEE Transactions on Information Theory)

Security/privacy for FL

Privacy-preserving, Attack, and Defense

[1][LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://arxiv.org/abs/2109.14236) (MLSys’22)

[2][Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://arxiv.org/abs/2002.04156) (JSAIT’21)

[3][Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning](https://arxiv.org/abs/2106.03328) (end-to-end privacy protection in FL)

[4][A scalable approach for privacy-preserving collaborative machine learning](https://arxiv.org/abs/2011.01963) (NeurIPS 2020)

[5][Secure aggregation for buffered asynchronous federated learning](https://arxiv.org/abs/2110.02177) (Arxiv’2021)

[6][Basil: A Fast and Byzantine-Resilient Approach for Decentralized Training](https://arxiv.org/abs/2109.07706)

[7][CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning](https://arxiv.org/abs/1902.01981) (IEEE/ACM Transactions on Networking)

[8][Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning](https://arxiv.org/abs/2107.12958) (IPDPS 2022)

[9][CodedPrivateML: A fast and privacy-preserving framework for distributed machine learning](https://ieeexplore.ieee.org/abstract/document/9330572) (IEEE Journal on Selected Areas in Information Theory)

[10][Byzantine-resilient secure federated learning](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:HtEfBTGE9r8C) (IEEE Journal on Selected Areas in Information Theory)

[11][Mitigating byzantine attacks in federated learning](https://www.researchgate.net/profile/Saurav-Prakash-2/publication/344678610_Mitigating_Byzantine_Attacks_in_Federated_Learning/links/609c37b292851cca5984d6b3/Mitigating-Byzantine-Attacks-in-Federated-Learning.pdf)

[12][Secure aggregation with heterogeneous quantization in federated learning](https://arxiv.org/abs/2009.14388)

[13][Entangled polynomial codes for secure, private, and batch distributed matrix multiplication: Breaking the” cubic” barrier](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:_axFR9aDTf0C) (ISIT 2020)

[14][Coded merkle tree: Solving data availability attacks in blockchains](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:Ug5p-4gJ2f0C) (International Conference on Financial Cryptography and Data Security)

[15][HeteroSAg: Secure Aggregation with Heterogeneous Quantization in Federated Learning](https://arxiv.org/abs/2009.14388)

[16][Polyshard: Coded sharding achieves linearly scaling efficiency and security simultaneously](https://ieeexplore.ieee.org/abstract/document/9141331) (IEEE Transactions on Information Forensics and Security)

AI Applications

Besides fundamental research in FL, we also target important applications in Natural Language Processing, Computer Vision, Data Mining, and the Internet of Things (IoTs).

[1][FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://arxiv.org/pdf/2104.08815.pdf) NAACL 2022

[2][FedGraphNN: A Federated Learning Benchmark System for Graph Neural Networks](https://arxiv.org/pdf/2104.07145.pdf) (ICLR 2021 workshop; KDD 2021 workshop)

[3][FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://arxiv.org/pdf/2111.11066.pdf) (FL-AAAI’2022)

[4][Federated Learning for Internet of Things](https://arxiv.org/pdf/2106.07976.pdf) (ACM Sensys’21)

[5][MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation](https://arxiv.org/abs/2003.12238) (CVPR 2020)

[6][AutoCTS: Automated Correlated Time Series Forecasting](https://arxiv.org/abs/2112.11174) (VLDB 2022)

[7][Coded computing for distributed graph analytics](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:uJ-U7cs_P_0C) (IEEE Transactions on Information Theory)

[8][TACC: Topology-aware coded computing for distributed graph processing](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:_FM0Bhl9EiAC) (IEEE Transactions on Signal and Information Processing over Networks)

[9][Privacy-Aware Distributed Graph-Based Semi-Supervised Learning](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Qhe5ua0AAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=Qhe5ua0AAAAJ:5qfkUJPXOUwC) (2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)

[10][Lightweight Image Super-Resolution with Hierarchical and Differentiable Neural Architecture Search](https://arxiv.org/abs/2105.03939) (IJCV Journal Under Review)

[11][Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling](https://ieeexplore.ieee.org/abstract/document/8852142) (IJCNN 2019)