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Full-Text Articles in Physical Sciences and Mathematics
Quantifying And Enhancing The Security Of Federated Learning, Virat Vishnu Shejwalkar
Quantifying And Enhancing The Security Of Federated Learning, Virat Vishnu Shejwalkar
Doctoral Dissertations
Federated learning is an emerging distributed learning paradigm that allows multiple users to collaboratively train a joint machine learning model without having to share their private data with any third party. Due to many of its attractive properties, federated learning has received significant attention from academia as well as industry and now powers major applications, e.g., Google's Gboard and Assistant, Apple's Siri, Owkin's health diagnostics, etc. However, federated learning is yet to see widespread adoption due to a number of challenges. One such challenge is its susceptibility to poisoning by malicious users who aim to manipulate the joint machine learning …
Concentration Inequalities In The Wild: Case Studies In Blockchain & Reinforcement Learning, A. Pinar Ozisik
Concentration Inequalities In The Wild: Case Studies In Blockchain & Reinforcement Learning, A. Pinar Ozisik
Doctoral Dissertations
Concentration inequalities (CIs) are a powerful tool that provide probability bounds on how a random variable deviates from its expectation. In this dissertation, first I describe a blockchain protocol that I have developed, called Graphene, which uses CIs to provide probabilistic guarantees on performance. Second, I analyze the extent to which CIs are robust when the assumptions they require are violated, using Reinforcement Learning (RL) as the domain. Graphene is a method for interactive set reconciliation among peers in blockchains and related distributed systems. Through the novel combination of a Bloom filter and an Invertible Bloom Lookup Table, Graphene uses …
Design And Implementation Of Path Finding And Verification In The Internet, Hao Cai
Design And Implementation Of Path Finding And Verification In The Internet, Hao Cai
Doctoral Dissertations
In the Internet, network traffic between endpoints typically follows one path that is determined by the control plane. Endpoints have little control over the choice of which path their network traffic takes and little ability to verify if the traffic indeed follows a specific path. With the emergence of software-defined networking (SDN), more control over connections can be exercised, and thus the opportunity for novel solutions exists. However, there remain concerns about the attack surface exposed by fine-grained control, which may allow attackers to inject and redirect traffic. To address these opportunities and concerns, we consider two specific challenges: (1) …