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Found 17 papers, 1 papers with code
Alopex: A Computational Framework for Enabling On-Device Function Calls with LLMs
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 API functions to enhance their performance.
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference
With the rapid growth of Large Language Models (LLMs) across various domains, numerous new LLMs have emerged, each possessing domain-specific expertise.
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
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.
TorchOpera: A Compound AI System for LLM Safety
We introduce TorchOpera, a compound AI system for enhancing the safety and quality of prompts and responses for Large Language Models.
MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows
The controller is responsible for managing the execution of FL workflows across learners and the learners for training and evaluating federated models over their private datasets.
Federated Learning over Harmonized Data Silos
Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sharing data.
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions.
Secure & Private Federated Neuroimaging
Each site trains the neural network over its private data for some time, then shares the neural network parameters (i. e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.
Performance Weighting for Robust Federated Learning Against Corrupted Sources
Here, we focus on the latter, the susceptibility of federated learning to various data corruption attacks.
Federated Progressive Sparsification (Purge, Merge, Tune)+
Second, the models are incrementally constrained to a smaller set of parameters, which facilitates alignment/merging of the local models and improved learning performance at high sparsification rates.
Federated Named Entity Recognition
We present an analysis of the performance of Federated Learning in a paradigmatic natural-language processing task: Named-Entity Recognition (NER).
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.
Membership Inference Attacks on Deep Regression Models for Neuroimaging
In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution.
Scaling Neuroscience Research using Federated Learning
Federated Learning is a promising approach to learn a joint model over data silos.
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings
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 privacy reasons.
Accelerating Federated Learning in Heterogeneous Data and Computational Environments
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 privacy reasons.
