354 citations · 835 across the 22 of their papers we have counts for
6 papers · 1 filter
Distributed Training of Graph Convolutional Networks using Subgraph Approximation
Alexandra Angerd, Keshav Balasubramanian, Murali Annavaram
Modern machine learning techniques are successfully being adapted to data modeled as graphs. However, many real-world graphs are typically very large and do not fit in memory, ofte…
Secure and Fault Tolerant Decentralized Learning
Saurav Prakash, Hanieh Hashemi, Yongqin Wang +2
Federated learning (FL) is a promising paradigm for training a global model over data distributed across multiple data owners without centralizing clients' raw data. However, shari…
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
Chaoyang He, Murali Annavaram, Salman Avestimehr
Scaling up the convolutional neural network (CNN) size (e.g., width, depth, etc.) is known to effectively improve model accuracy. However, the large model size impedes training on…
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsist…
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Protecting the privacy of input data is of growing importance as machine learning methods reach new application domains. In this paper, we provide a unified training and inference…
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search
Chaoyang He, Murali Annavaram, Salman Avestimehr
Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions. When…