356 citations · 457 across the 23 of their papers we have counts for
4 papers · 1 filter
Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control
Fabian Ruffy, Michael Przystupa, Ivan Beschastnikh
Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular…
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…
Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting
Clement Fung, Jamie Koerner, Stewart Grant +1
Distributed machine learning (ML) systems today use an unsophisticated threat model: data sources must trust a central ML process. We propose a brokered learning abstraction that a…
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…