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Distributed Systems

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Distributed Systems

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Research Frontiers in Distributed Machine Learning Model Training

Study of techniques for parallelizing and coordinating machine learning model training across heterogeneous computing nodes.

Gradient Staleness and Convergence in Asynchronous Learning
Byzantine-Resilient Consensus for Federated Neural Networks
Communication-Optimal Compression in Distributed Deep Learning
Heterogeneous Data Distributions Across Decentralized Training Networks
Incentive Mechanisms for Truthful Participation in Collaborative Learning
Model Poisoning Detection in Peer-to-Peer Training Environments
Differential Privacy at Scale in Federated Architectures
Resource-Aware Scheduling for Edge-Cloud Model Training
Topology-Aware Gossip Protocols for Parameter Synchronization

All Distributed Systems PhD categories