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Jie Ding

10 papers hereh-index 7224 citations20 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author6
  • last author2

Across the 9 of 10 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.CR1
  • cs.CV1
same name
  • Jie Ding — 47 papers, h 14
  • Jie Ding — 10 papers, h 17
  • Jie Ding — 10 papers, h 6
  • Jie Ding — 8 papers, h 6
  • Jie Ding — 7 papers, h 7
  • Jie Ding — 7 papers, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212023
most citedSPIDER: Searching Personalized Neural Architecture for Federated Learning

7 citations · 26 across the 10 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.LG2023★ 2 cited

A Framework for Incentivized Collaborative Learning

Xinran Wang, Qi Le, Ahmad Faraz Khan +2

Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that can…

cs.LG2023

Semi-Supervised Federated Learning for Keyword Spotting

Enmao Diao, Eric W. Tramel, Jie Ding +1

Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants. Recent developments in Federated Learning (FL) have significantly…

cs.CV2023★ 1 cited

Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging

Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding +1

Federated Learning (FL) aims to train a machine learning (ML) model in a distributed fashion to strengthen data privacy with limited data migration costs. It is a distributed learn…

cs.LG2023★ 5 cited

IP-FL: Incentivized and Personalized Federated Learning

Ahmad Faraz Khan, Xinran Wang, Qi Le +7

Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personaliza…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.