10 citations · 20 across the 11 of their papers we have counts for
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cs.LG2022★ 8 cited
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…
cs.LG2021★ 10 cited
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…