fix fedtrans paper (#254)

* fix fedtrans paper

* Update FedTrans authors list

---------

Co-authored-by: Mosharaf Chowdhury <[email protected]>

Author: AmberLJC

source/_data/SymbioticLab.bib

@@ -1724,12 +1724,13 @@ @Article{crosslayer-energy:arxiv24
 }
 
 @InProceedings{fedtrans:mlsys24,
-  author    = {Yuxuan Zhu and Jiachen Liu and Fan Lai and Mosharaf Chowdhury},
+  author    = {Yuxuan Zhu and Jiachen Liu and Mosharaf Chowdhury and Fan Lai},
   booktitle = {MLSys},
   title     = {{FedTrans}: Efficient Federated Learning via Multi-Model Transformation},
   year      = {2024},	
   publist_confkey = {MLSys'24},
   publist_topic = {Systems + AI},
+  publist_link = {paper || fedtrans-mlsys24.pdf},
   publist_topic = {Wide-Area Computing},
   publist_abstract = {
 Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices. Yet, training and customizing models for FL clients is notoriously challenging due to the heterogeneity of client data, device capabilities, and the massive scale of clients, making individualized model exploration prohibitively expensive. State-of-the-art FL solutions personalize a globally trained model or concurrently train multiple models, but they often incur suboptimal model accuracy and huge training costs.