Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Decouchant, Jeremie"'
Streamlined Byzantine Fault Tolerant (BFT) protocols, such as HotStuff [PODC'19], and weighted voting represent two possible strategies to improve consensus in the distributed systems world. Several studies have been conducted on both techniques, but
Externí odkaz:
http://arxiv.org/abs/2410.21923
Publikováno v:
2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), Jul 2024, Jersey City, France. pp.139-150
Detecting and handling network partitions is a fundamental requirement of distributed systems. Although existing partition detection methods in arbitrary graphs tolerate unreliable networks, they either assume that all nodes are correct or that a lim
Externí odkaz:
http://arxiv.org/abs/2408.14814
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 powerful communication primitives, such as broadcast and consensus. Using di
Externí odkaz:
http://arxiv.org/abs/2408.08060
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 authority due to their lack of transparency regarding training data. To ad
Externí odkaz:
http://arxiv.org/abs/2406.12575
Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can also evolve with time, which calls for the integration of methodologies
Externí odkaz:
http://arxiv.org/abs/2406.02015
Federated learning (FL) systems enable multiple clients to train a machine learning model iteratively through synchronously exchanging the intermediate model weights with a single server. The scalability of such FL systems can be limited by two facto
Externí odkaz:
http://arxiv.org/abs/2406.01439
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 can be made asynchronous to maintain its speed in presence of slow clien
Externí odkaz:
http://arxiv.org/abs/2406.01438
Vertical federated learning (VFL) is a promising area for time series forecasting in industrial applications, such as predictive maintenance and machine control. Critical challenges to address in manufacturing include data privacy and over-fitting on
Externí odkaz:
http://arxiv.org/abs/2405.20761
Effective large-scale process optimization in manufacturing industries requires close cooperation between different human expert parties who encode their knowledge of related domains as Bayesian network models. For instance, Bayesian networks for dom
Externí odkaz:
http://arxiv.org/abs/2405.15055
Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for
Externí odkaz:
http://arxiv.org/abs/2404.17990