4 papers
Distributed Mixture-of-Agents for Edge Inference with Large Language Models
Purbesh Mitra, Priyanka Kaswan, Sennur Ulukus
Mixture-of-Agents (MoA) has recently been proposed as a method to enhance performance of large language models (LLMs), enabling multiple individual LLMs to work together for collab…
Age-: Communication-Efficient Federated Learning Using Age Factor
Matin Mortaheb, Priyanka Kaswan, Sennur Ulukus
Federated learning (FL) is a collaborative approach where multiple clients, coordinated by a parameter server (PS), train a unified machine-learning model. The approach, however, s…
Optimizing Profitability in Timely Gossip Networks
Priyanka Kaswan, Melih Bastopcu, Sennur Ulukus +2
We consider a communication system where a group of users, interconnected in a bidirectional gossip network, wishes to follow a time-varying source, e.g., updates on an event, in r…
How to Make Money From Fresh Data: Subscription Strategies in Age-Based Systems
Priyanka Kaswan, Melih Bastopcu, Sennur Ulukus +2
We consider a communication system consisting of a server that tracks and publishes updates about a time-varying data source or event, and a gossip network of users interested in c…