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cs.LG2024★ 2 cited
Personalized Federated Learning via Feature Distribution Adaptation
Connor J. Mclaughlin, Lili Su
Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. Under heterogeneous clients, h…
cs.LG2023
Mahalanobis-Aware Training for Out-of-Distribution Detection
Connor Mclaughlin, Jason Matterer, Michael Yee
While deep learning models have seen widespread success in controlled environments, there are still barriers to their adoption in open-world settings. One critical task for safe de…
cs.LG2023
Network Fault-tolerant and Byzantine-resilient Social Learning via Collaborative Hierarchical Non-Bayesian Learning
Connor Mclaughlin, Matthew Ding, Denis Edogmus +1
As the network scale increases, existing fully distributed solutions start to lag behind the real-world challenges such as (1) slow information propagation, (2) network communicati…