42 citations · 59 across the 15 of their papers we have counts for
18 papers
A Technical Policy Blueprint for Trustworthy Decentralized AI
Hasan Kassem, Orion Banks, Omar Benjelloun +20
Decentralized AI systems, such as federated learning, can play a critical role in further unlocking AI asset marketplaces (e.g., healthcare data marketplaces) thanks to increased a…
Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts
Ramesh Raskar, Pradyumna Chari, John Zinky +15
The Internet is poised to host billions to trillions of autonomous AI agents that negotiate, delegate, and migrate in milliseconds and workloads that will strain DNS-centred identi…
Upgrade or Switch: Do We Need a Next-Gen Trusted Architecture for the Internet of AI Agents?
Ramesh Raskar, Pradyumna Chari, Jared James Grogan +11
The emerging Internet of AI Agents challenges existing web infrastructure designed for human-scale, reactive interactions. Unlike traditional web resources, autonomous AI agents in…
Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs
Yide Ran, Zhaozhuo Xu, Yuhang Yao +9
The rapid advancement of Large Language Models (LLMs) has led to their increased integration into mobile devices for personalized assistance, which enables LLMs to call external AP…
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
Yuhang Yao, Han Jin, Alay Dilipbhai Shah +7
Large language models (LLMs) have surged in popularity and are extensively used in commercial applications, where the efficiency of model serving is crucial for the user experience…
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference
Dimitris Stripelis, Zijian Hu, Jipeng Zhang +6
With the rapid growth of Large Language Models (LLMs) across various domains, numerous new LLMs have emerged, each possessing domain-specific expertise. This proliferation has high…
TorchOpera: A Compound AI System for LLM Safety
Shanshan Han, Zijian Hu, Alay Dilipbhai Shah +5
We introduce TorchOpera, a compound AI system for enhancing the safety and quality of prompts and responses for Large Language Models. TorchOpera ensures that all user prompts are…
MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows
Dimitris Stripelis, Chrysovalantis Anastasiou, Patrick Toral +2
A Federated Learning (FL) system typically consists of two core processing entities: the federation controller and the learners. The controller is responsible for managing the exec…
Federated Learning over Harmonized Data Silos
Dimitris Stripelis, Jose Luis Ambite
Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sha…
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar +3
Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model prunin…
Performance Weighting for Robust Federated Learning Against Corrupted Sources
Dimitris Stripelis, Marcin Abram, Jose Luis Ambite
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning. Its unique data privacy properties allow us to collaboratively train models wh…
Secure & Private Federated Neuroimaging
Dimitris Stripelis, Umang Gupta, Hamza Saleem +9
The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulator…
Federated Progressive Sparsification (Purge, Merge, Tune)+
Dimitris Stripelis, Umang Gupta, Greg Ver Steeg +1
To improve federated training of neural networks, we develop FedSparsify, a sparsification strategy based on progressive weight magnitude pruning. Our method has several benefits.…
Federated Named Entity Recognition
Joel Mathew, Dimitris Stripelis, José Luis Ambite
We present an analysis of the performance of Federated Learning in a paradigmatic natural-language processing task: Named-Entity Recognition (NER). For our evaluation, we use the l…
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Dimitris Stripelis, Hamza Saleem, Tanmay Ghai +8
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to…
Scaling Neuroscience Research using Federated Learning
Dimitris Stripelis, Jose Luis Ambite, Pradeep Lam +1
The amount of biomedical data continues to grow rapidly. However, the ability to analyze these data is limited due to privacy and regulatory concerns. Machine learning approaches t…
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings
Dimitris Stripelis, Jose Luis Ambite
There are situations where data relevant to machine learning problems are distributed across multiple locations that cannot share the data due to regulatory, competitiveness, or pr…
Accelerating Federated Learning in Heterogeneous Data and Computational Environments
Dimitris Stripelis, Jose Luis Ambite
There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or pr…