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. 2405.10968
  4. Cited By
LIFL: A Lightweight, Event-driven Serverless Platform for Federated
  Learning

LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning

5 May 2024
Shixiong Qi
K. K. Ramakrishnan
Myungjin Lee
ArXivPDFHTML

Papers citing "LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning"

15 / 15 papers shown
Title
Flame: Simplifying Topology Extension in Federated Learning
Flame: Simplifying Topology Extension in Federated Learning
Harshit Daga
Jae-Kwang Shin
D. Garg
Ada Gavrilovska
Myungjin Lee
Ramana Rao Kompella
AI4CE
59
10
0
09 May 2023
Auxo: Efficient Federated Learning via Scalable Client Clustering
Auxo: Efficient Federated Learning via Scalable Client Clustering
Jiachen Liu
Fan Lai
Yinwei Dai
Aditya Akella
H. Madhyastha
Mosharaf Chowdhury
97
10
0
29 Oct 2022
Adaptive Aggregation For Federated Learning
Adaptive Aggregation For Federated Learning
K.R. Jayaram
Vinod Muthusamy
Gegi Thomas
Ashish Verma
Mark Purcell
FedML
79
17
0
23 Mar 2022
FedBalancer: Data and Pace Control for Efficient Federated Learning on
  Heterogeneous Clients
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Jaemin Shin
Yuanchun Li
Yunxin Liu
Sung-Ju Lee
FedML
50
74
0
05 Jan 2022
FedLess: Secure and Scalable Federated Learning Using Serverless
  Computing
FedLess: Secure and Scalable Federated Learning Using Serverless Computing
Andreas Grafberger
Mohak Chadha
Anshul Jindal
Jianfeng Gu
Michael Gerndt
62
51
0
05 Nov 2021
FedScale: Benchmarking Model and System Performance of Federated
  Learning at Scale
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai
Yinwei Dai
Sanjay Sri Vallabh Singapuram
Jiachen Liu
Xiangfeng Zhu
H. Madhyastha
Mosharaf Chowdhury
FedML
98
201
0
24 May 2021
Benchmarking, Analysis, and Optimization of Serverless Function
  Snapshots
Benchmarking, Analysis, and Optimization of Serverless Function Snapshots
Dmitrii Ustiugov
Plamen Petrov
Marios Kogias
Edouard Bugnion
Boris Grot
57
176
0
16 Jan 2021
FedML: A Research Library and Benchmark for Federated Machine Learning
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He
Songze Li
Jinhyun So
Xiao Zeng
Mi Zhang
...
Yang Liu
Ramesh Raskar
Qiang Yang
M. Annavaram
Salman Avestimehr
FedML
226
577
0
27 Jul 2020
Serverless in the Wild: Characterizing and Optimizing the Serverless
  Workload at a Large Cloud Provider
Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider
Mohammad Shahrad
Rodrigo Fonseca
Íñigo Goiri
G. Chaudhry
Paul Batum
Jason Cooke
Eduardo Laureano
Colby Tresness
M. Russinovich
Ricardo Bianchini
119
618
0
06 Mar 2020
Adaptive Federated Optimization
Adaptive Federated Optimization
Sashank J. Reddi
Zachary B. Charles
Manzil Zaheer
Zachary Garrett
Keith Rush
Jakub Konecný
Sanjiv Kumar
H. B. McMahan
FedML
174
1,435
0
29 Feb 2020
Faasm: Lightweight Isolation for Efficient Stateful Serverless Computing
Faasm: Lightweight Isolation for Efficient Stateful Serverless Computing
Simon Shillaker
Peter R. Pietzuch
66
281
0
21 Feb 2020
Mitigating Cold Starts in Serverless Platforms: A Pool-Based Approach
Mitigating Cold Starts in Serverless Platforms: A Pool-Based Approach
Ping-Min Lin
A. Glikson
25
69
0
28 Mar 2019
Towards Federated Learning at Scale: System Design
Towards Federated Learning at Scale: System Design
Keith Bonawitz
Hubert Eichner
W. Grieskamp
Dzmitry Huba
A. Ingerman
...
H. B. McMahan
Timon Van Overveldt
David Petrou
Daniel Ramage
Jason Roselander
FedML
121
2,664
0
04 Feb 2019
Peer-to-peer Federated Learning on Graphs
Peer-to-peer Federated Learning on Graphs
Anusha Lalitha
O. Kilinc
T. Javidi
F. Koushanfar
OOD
FedML
96
186
0
31 Jan 2019
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
397
17,468
0
17 Feb 2016
1