Search Results for author:
Found 6 papers, 2 papers with code
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised Learning
Two main challenges of continual learning are catastrophic forgetting and task confusion.
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging
It is a distributed learning framework naturally suitable for privacy-sensitive medical imaging datasets.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
SPIDER: Searching Personalized Neural Architecture for Federated Learning
However, given that clients' data are invisible to the server and data distributions are non-identical across clients, a predefined architecture discovered in a centralized setting may not be an optimal solution for all the clients in FL.
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision
In this paper we propose self-supervised federated learning (SSFL), a unified self-supervised and personalized federated learning framework, and a series of algorithms under this framework which work towards addressing these challenges.
FedNAS: Federated Deep Learning via Neural Architecture Search
Federated Learning (FL) is an effective learning framework used when data cannotbe centralized due to privacy, communication costs, and regulatory restrictions. While there have been many algorithmic advances in FL, significantly less effort hasbeen made on model development, and most works in FL employ predefined modelarchitectures discovered in the centralized environment.
