60 citations · 105 across the 16 of their papers we have counts for
3 papers · 1 filter
PGFed: Personalize Each Client's Global Objective for Federated Learning
Jun Luo, Matias Mendieta, Chen Chen +1
Personalized federated learning has received an upsurge of attention due to the mediocre performance of conventional federated learning (FL) over heterogeneous data. Unlike convent…
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
Guangyu Sun, Umar Khalid, Matias Mendieta +2
Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the ty…
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Matias Mendieta, Taojiannan Yang, Pu Wang +3
Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distributi…