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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.
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.
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.
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.
NSEEN: Neural Semantic Embedding for Entity Normalization
An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets.
Learning the Semantics of Structured Data Sources
This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology.
