Federated Learning for Inference at Anytime and Anywhere

Federated Learning for Inference at Anytime and Anywhere

8 December 2022

Zicheng Liu

Javier Fernandez-Marques

Stefanos Laskaridis

Stan Z. Li

Timothy M. Hospedales

Papers citing "Federated Learning for Inference at Anytime and Anywhere"

9 / 9 papers shown

Title
Federated Large Language Models: Current Progress and Future Directions Yuhang Yao Jianyi Zhang Junda Wu Chengkai Huang Yu Xia ... Ang Li Lina Yao Julian McAuley Yiran Chen Carlee Joe-Wong FedML AIFin 61 8 0 24 Sep 2024
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition Samuel Horváth Stefanos Laskaridis Shashank Rajput Hongyi Wang BDL 26 4 0 28 Aug 2023
FedYolo: Augmenting Federated Learning with Pretrained Transformers Xuechen Zhang Mingchen Li Xiangyu Chang Jiasi Chen A. Roy-Chowdhury A. Suresh Samet Oymak FedML 18 7 0 10 Jul 2023
On the Efficacy of Differentially Private Few-shot Image Classification Marlon Tobaben Aliaksandra Shysheya J. Bronskill Andrew J. Paverd Shruti Tople Santiago Zanella Béguelin Richard E. Turner Antti Honkela 12 11 0 02 Feb 2023
Federated Progressive Sparsification (Purge, Merge, Tune)+ Dimitris Stripelis Umang Gupta Greg Ver Steeg J. Ambite FedML 13 9 0 26 Apr 2022
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech Katrin Tomanek Vicky Zayats Dirk Padfield K. Vaillancourt Fadi Biadsy 51 57 0 14 Sep 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,835 0 18 Apr 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout Samuel Horváth Stefanos Laskaridis Mario Almeida Ilias Leondiadis Stylianos I. Venieris Nicholas D. Lane 176 267 0 26 Feb 2021
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation Ilias Leontiadis Stefanos Laskaridis Stylianos I. Venieris Nicholas D. Lane 63 29 0 02 Feb 2021