2 citations · 3 across the 17 of their papers we have counts for
9 papers · 1 filter
Optimizing Streamlined Blockchain Consensus with Generalized Weighted Voting and Enhanced Leader Rotation
Diana Micloiu, Rowdy Chotkan, Jérémie Decouchant
Streamlined Byzantine Fault Tolerant (BFT) protocols, such as HotStuff [PODC'19], and weighted voting represent two possible strategies to improve consensus in the distributed syst…
Partition Detection in Byzantine Networks
Yérom-David Bromberg, Jérémie Decouchant, Manon Sourisseau +1
Detecting and handling network partitions is a fundamental requirement of distributed systems. Although existing partition detection methods in arbitrary graphs tolerate unreliable…
Reliable Communication in Hybrid Authentication and Trust Models
Rowdy Chotkan, Bart Cox, Vincent Rahli +1
Reliable communication is a fundamental distributed communication abstraction that allows any two nodes of a network to communicate with each other. It is necessary for more powerf…
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…