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Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings


Dec 26, 2021
Tiantian Feng, Hanieh Hashemi, Rajat Hebbar, Murali Annavaram, Shrikanth S. Narayanan


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Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning


Jul 27, 2021
Tingting Tang, Ramy E. Ali, Hanieh Hashemi, Tynan Gangwani, Salman Avestimehr, Murali Annavaram


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SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks


Jun 04, 2021
Chaoyang He, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram, Salman Avestimehr

* Three co-1st authors have equal contribution (alphabetical order) 

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Byzantine-Robust and Privacy-Preserving Framework for FedML


May 05, 2021
Hanieh Hashemi, Yongqin Wang, Chuan Guo, Murali Annavaram

* Security and Safety in Machine Learning Systems Workshop in ICLR 2021 

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Privacy and Integrity Preserving Training Using Trusted Hardware


May 01, 2021
Hanieh Hashemi, Yongqin Wang, Murali Annavaram

* Distributed and Private Machine Learning ICLR 2021 Workshop 

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FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks


Apr 14, 2021
Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

* The first three authors contribute equally. Our shorter versions are accepted to ICLR 2021 Workshop on Distributed and Private Machine Learning(DPML) and MLSys 2021 GNNSys Workshop on Graph Neural Networks and Systems 

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Distributed Training of Graph Convolutional Networks using Subgraph Approximation


Dec 09, 2020
Alexandra Angerd, Keshav Balasubramanian, Murali Annavaram


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Check-N-Run: A Checkpointing System for Training Recommendation Models


Oct 17, 2020
Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Murali Annavaram, Krishnakumar Nair, Misha Smelyanskiy


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Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge


Jul 30, 2020
Chaoyang He, Murali Annavaram, Salman Avestimehr

* This paper attempts to address one of the core problems of federated learning: training deep neural networks in resource-constrained edge devices 

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FedML: A Research Library and Benchmark for Federated Machine Learning


Jul 27, 2020
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr

* We maintain the source code, documents, and user community at https://fedml.ai 

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FedNAS: Federated Deep Learning via Neural Architecture Search


Apr 18, 2020
Chaoyang He, Murali Annavaram, Salman Avestimehr

* accepted to CVPR 2020 workshop on neural architecture search and beyond for representation learning 

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Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments


Dec 07, 2019
Krishna Giri Narra, Zhifeng Lin, Yongqin Wang, Keshav Balasubramaniam, Murali Annavaram

* 13 pages, Under submission 

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Train Where the Data is: A Case for Bandwidth Efficient Coded Training


Oct 22, 2019
Zhifeng Lin, Krishna Giri Narra, Mingchao Yu, Salman Avestimehr, Murali Annavaram

* 10 pages, Under submission 

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Collage Inference: Achieving low tail latency during distributed image classification using coded redundancy models


Jun 05, 2019
Krishna Narra, Zhifeng Lin, Ganesh Ananthanarayanan, Salman Avestimehr, Murali Annavaram

* 4 pages, CodML workshop at International Conference on Machine Learning (ICML 2019). arXiv admin note: text overlap with arXiv:1904.12222 

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Collage Inference: Tolerating Stragglers in Distributed Neural Network Inference using Coding


Apr 27, 2019
Krishna Giri Narra, Zhifeng Lin, Ganesh Ananthanarayanan, Salman Avestimehr, Murali Annavaram

* 13 pages, 13 figures, Under submission 

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GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training


Nov 08, 2018
Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander Schwing, Murali Annavaram, Salman Avestimehr


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