The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

Byzantine resilience emerged as a prominent topic within the distributed machine learning
community. Essentially, the goal is to enhance distributed optimization algorithms, such as …

Decentralized learning involves training machine learning models over remote mobile
devices, edge servers, or cloud servers while keeping data localized. Even though many …

Federated Learning (FL) offers innovative solutions for privacy-preserving collaborative
machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks …

We study\emph {Byzantine collaborative learning}, where $ n $ nodes seek to collectively
learn from each others' local data. The data distribution may vary from one node to another …

Abstract Byzantine-resilient Stochastic Gradient Descent (SGD) aims at shielding model
training from Byzantine faults, be they ill-labeled training datapoints, exploited …

Federated Learning (FL) enables many clients to train a joint model without sharing the raw
data. While many byzantine-robust FL methods have been proposed, FL remains vulnerable …

To study the resilience of distributed learning, the “Byzantine" literature considers a strong
threat model where workers can report arbitrary gradients to the parameter server. Whereas …

Multidimensional Approximate Agreement considers a setting of n parties, where each party
holds a vector in ℝD as input. The honest parties are required to obtain very close outputs in …

Industry 5.0 is emerging as a result of the advancement in networking and communication
technologies, artificial intelligence, distributed computing, and beyond 5G. Among the …