356 citations · 457 across the 23 of their papers we have counts for
6 papers · 1 filter
Scalable Data Point Valuation in Decentralized Learning
Konstantin D. Pandl, Chun-Yin Huang, Ivan Beschastnikh +3
Existing research on data valuation in federated and swarm learning focuses on valuing client contributions and works best when data across clients is independent and identically d…
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Shiqi He, Qifan Yan, Feijie Wu +3
Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communicat…
Generalizing Neural Networks by Reflecting Deviating Data in Production
Yan Xiao, Yun Lin, Ivan Beschastnikh +3
Trained with a sufficiently large training and testing dataset, Deep Neural Networks (DNNs) are expected to generalize. However, inputs may deviate from the training dataset distri…
Fairness-guided SMT-based Rectification of Decision Trees and Random Forests
Jiang Zhang, Ivan Beschastnikh, Sergey Mechtaev +1
Data-driven decision making is gaining prominence with the popularity of various machine learning models. Unfortunately, real-life data used in machine learning training may captur…
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
Muhammad Shayan, Clement Fung, Chris J. M. Yoon +1
Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are…
Mitigating Sybils in Federated Learning Poisoning
Clement Fung, Chris J. M. Yoon, Ivan Beschastnikh
Machine learning (ML) over distributed multi-party data is required for a variety of domains. Existing approaches, such as federated learning, collect the outputs computed by a gro…