ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2410.17961
  4. Cited By
Closed-form merging of parameter-efficient modules for Federated Continual Learning

Closed-form merging of parameter-efficient modules for Federated Continual Learning

23 October 2024
Riccardo Salami
Pietro Buzzega
Matteo Mosconi
Jacopo Bonato
Luigi Sabetta
Simone Calderara
    FedML
    MoMe
    CLL
ArXivPDFHTML

Papers citing "Closed-form merging of parameter-efficient modules for Federated Continual Learning"

2 / 2 papers shown
Title
FedMerge: Federated Personalization via Model Merging
FedMerge: Federated Personalization via Model Merging
Shutong Chen
Tianyi Zhou
Guodong Long
Jing Jiang
Chengqi Zhang
FedML
MoMe
43
0
0
09 Apr 2025
DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection
Chiara Cappellino
G. Mancusi
Matteo Mosconi
Angelo Porrello
Simone Calderara
Rita Cucchiara
ObjD
VLM
76
0
0
12 Mar 2025
1