• Publications
  • Influence
Advances and Open Problems in Federated Learning
TLDR
Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges. Expand
Split learning for health: Distributed deep learning without sharing raw patient data
TLDR
This paper compares performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN. Expand
FedML: A Research Library and Benchmark for Federated Machine Learning
TLDR
FedML is introduced, an open research library and benchmark that facilitates the development of new federated learning algorithms and fair performance comparisons and can provide an efficient and reproducible means of developing and evaluating algorithms for the Federated learning research community. Expand
Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic
TLDR
The different technological approaches to mobile-phone based contact-tracing to date are outlined and advanced security enhancing approaches that can mitigate these risks are described and trade-offs one must make are described. Expand
Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy
TLDR
This work aims to propose a location-based system that is more privacy-preserving than what is currently being adopted by governments around the world, and that is also practical to implement with the immediacy needed to stem a viral outbreak. Expand
A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities
TLDR
The designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables and makes it feasible to classify large number of fine-grained and complex activities, through Deep Learning based data analytics and exploiting multi-modal sensing on wrist-worn device. Expand
Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic
TLDR
This work offers an alternative approach to assess and communicate users’ risk of exposure to an infectious disease while preserving individual privacy, using recent GPS location histories, which are transformed and encrypted, and a private set intersection protocol. Expand
Detailed comparison of communication efficiency of split learning and federated learning
TLDR
This work considers various practical scenarios of distributed learning setup and juxtapose the two methods under various real-life scenarios and shows useful settings under which each method outperforms the other in terms of communication efficiency. Expand
R EDUCING LEAKAGE IN DISTRIBUTED DEEP LEARNING FOR SENSITIVE HEALTH DATA
For distributed machine learning with health data we demonstrate how minimizing distance correlation between raw data and intermediary representations (smashed data) reduces leakage of sensitive rawExpand
Split Learning for collaborative deep learning in healthcare
TLDR
This work proves the significant benefit of distributed learning in healthcare, and paves the way for future real-world implementations of split learning based approach in the medical field. Expand
...
1
2
3
4
5
...