8 citations · 17 across the 10 of their papers we have counts for
10 papers
Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks
Rajarshi Saha, Mohamed Seif, Michal Yemini +2
We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fai…
Compressing Large Language Models using Low Rank and Low Precision Decomposition
Rajarshi Saha, Naomi Sagan, Varun Srivastava +2
The prohibitive sizes of Large Language Models (LLMs) today make it difficult to deploy them on memory-constrained edge devices. This work introduces -- a new post-tr…
Matrix Compression via Randomized Low Rank and Low Precision Factorization
Rajarshi Saha, Varun Srivastava, Mert Pilanci
Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner. However, modern matrices…
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
Rajarshi Saha, Mohamed Seif, Michal Yemini +2
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data sampl…
Semi-Decentralized Federated Learning with Collaborative Relaying
Michal Yemini, Rajarshi Saha, Emre Ozfatura +2
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every com…
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying
Michal Yemini, Rajarshi Saha, Emre Ozfatura +2
Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks. The lack of constant connectivity induces a large gen…
Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms
Rajarshi Saha, Mert Pilanci, Andrea J. Goldsmith
High-dimensional models often have a large memory footprint and must be quantized after training before being deployed on resource-constrained edge devices for inference tasks. In…
Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams
Erdem Bıyık, Anusha Lalitha, Rajarshi Saha +2
When humans collaborate with each other, they often make decisions by observing others and considering the consequences that their actions may have on the entire team, instead of g…
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget
Rajarshi Saha, Mert Pilanci, Andrea J. Goldsmith
We study first-order optimization algorithms under the constraint that the descent direction is quantized using a pre-specified budget of -bits per dimension, where $R \in (0 ,\…
Decentralized optimization over noisy, rate-constrained networks: Achieving consensus by communicating differences
Rajarshi Saha, Stefano Rini, Milind Rao +1
In decentralized optimization, multiple nodes in a network collaborate to minimize the sum of their local loss functions. The information exchange between nodes required for this t…