Defending Distributed Systems Against Adversarial Attacks: Consensus, Consensusbased Learning, and Statistical Learning
By Lili Su. 2020
14 citations recorded by the source corpus.
Brief Biography: I am a postdoc in the Computer Sci- ence and Arti cial Intelligence Laboratory (CSAIL) at MIT, hosted by Professor Nancy Lynch. She received a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017, supervised by Pro- fessor Nitin H. Vaidya. Her research intersects distributed systems, learning, security, and brain computing. She was the runner-up for the Best Student Paper Award at DISC 2016, and she received the 2015 Best Student Paper Award at SSS 2015. She received UIUC's Sundaram Seshu Interna- tional Student Fellowship for 2016, and was invited to par- ticipate in Rising Stars in EECS (2018). She has served on TPC for several conferences including ICDCS and ICDCN.
Subfields: Computer Networks and Communications, Information Systems, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Georgios Damaskinos
- El-Mahdi El-Mhamdi
- Rachid Guerraoui
- Mahsa Taziki
- Sébastien Rouault
- El Mahdi El Mhamdi
In Information Systems
In Signal Processing
Citations in the corpus
- AGGREGATHOR: Byzantine Machine Learning via Robust Gradient Aggregation (2019, background): “A very recent line of theoretical research has addressed the problem of Byzantine-resilient SGD (Blanchard et al., 2017; El Mhamdi et al., 2018; Su, 2017; Xie et al., 2018; Yin et al., 2018).”
- Asynchronous Byzantine Machine Learning (2018, background): “A few Byzantine distributed ML solutions have been recently proposed (Blanchard et al., 2017; Su, 2017; El Mhamdi et al., 2018).”