2 citations · 3 across the 17 of their papers we have counts for
9 papers · 1 filter
Diffusion LLMs as Targets and Adversaries: Mechanistic Safety Exploits
Elena Dumitrescu, Gert Lek, Lydia Y. Chen +1
Diffusion Large Language Models (DLLMs) replace autoregressive next-token prediction with iterative parallel denoising, yet their internal safety mechanisms remain poorly understoo…
Dynamic Topology Optimization for Non-IID Data in Decentralized Learning
Bart Cox, Antreas Ioannou, Jérémie Decouchant
Decentralized learning (DL) enables a set of nodes to train a model collaboratively without central coordination, offering benefits for privacy and scalability. However, DL struggl…
Go With The Flow: Churn-Tolerant Decentralized Training of Large Language Models
Nikolay Blagoev, Bart Cox, Jérémie Decouchant +1
Motivated by the emergence of large language models (LLMs) and the importance of democratizing their training, we propose GWTF, the first crash tolerant practical decentralized tra…
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
Yuncong Zuo, Bart Cox, Lydia Y. Chen +1
Federated learning (FL) systems enable multiple clients to train a machine learning model iteratively through synchronously exchanging the intermediate model weights with a single…
Asynchronous Byzantine Federated Learning
Bart Cox, Abele Mălan, Lydia Y. Chen +1
Federated learning (FL) enables a set of geographically distributed clients to collectively train a model through a server. Classically, the training process is synchronous, but ca…
Training Diffusion Models with Federated Learning
Matthijs de Goede, Bart Cox, Jérémie Decouchant
The training of diffusion-based models for image generation is predominantly controlled by a select few Big Tech companies, raising concerns about privacy, copyright, and data auth…
Parameterizing Federated Continual Learning for Reproducible Research
Bart Cox, Jeroen Galjaard, Aditya Shankar +2
Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can a…
CCBNet: Confidential Collaborative Bayesian Networks Inference
Abele Mălan, Jérémie Decouchant, Thiago Guzella +1
Effective large-scale process optimization in manufacturing industries requires close cooperation between different human expert parties who encode their knowledge of related domai…
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning
Aditya Shankar, Jérémie Decouchant, Dimitra Gkorou +2
Vertical federated learning (VFL) is a promising area for time series forecasting in many applications, such as healthcare and manufacturing. Critical challenges to address include…