5 papers
Collaborative Compressors in Distributed Mean Estimation with Limited Communication Budget
Harsh Vardhan, Arya Mazumdar
Distributed high dimensional mean estimation is a common aggregation routine used often in distributed optimization methods. Most of these applications call for a communication-con…
LocalKMeans: Convergence of Lloyd's Algorithm with Distributed Local Iterations
Harsh Vardhan, Heng Zhu, Avishek Ghosh +1
In this paper, we analyze the classical -means alternating-minimization algorithm, also known as Lloyd's algorithm (Lloyd, 1956), for a mixture of Gaussians in a data-distribute…
Learning and Generalization with Mixture Data
Harsh Vardhan, Avishek Ghosh, Arya Mazumdar
In many, if not most, machine learning applications the training data is naturally heterogeneous (e.g. federated learning, adversarial attacks and domain adaptation in neural net t…
Client Selection in Federated Learning with Data Heterogeneity and Network Latencies
Harsh Vardhan, Xiaofan Yu, Tajana Rosing +1
Federated learning (FL) is a distributed machine learning paradigm where multiple clients conduct local training based on their private data, then the updated models are sent to a…
Distributed Hybrid Sketching for -Embeddings
Neophytos Charalambides, Arya Mazumdar
Linear algebraic operations are ubiquitous in engineering applications, and arise often in a variety of fields including statistical signal processing and machine learning. With co…