Distilled one-shot federated learning
Current federated learning algorithms take tens of communication rounds transmitting
unwieldy model weights under ideal circumstances and hundreds when data is poorly …
unwieldy model weights under ideal circumstances and hundreds when data is poorly …
CoPiFL: A collusion-resistant and privacy-preserving federated learning crowdsourcing scheme using blockchain and homomorphic encryption
R Xiong, W Ren, S Zhao, J He, Y Ren… - Future Generation …, 2024 - Elsevier
Federated learning (FL) is one of many tasks facilitated by crowdsourcing. Generally in such
a setting, participating workers cooperate to train a comprehensive model by exchanging the …
a setting, participating workers cooperate to train a comprehensive model by exchanging the …
Understanding and improving model averaging in federated learning on heterogeneous data
Model averaging is a widely adopted technique in federated learning (FL) that aggregates
multiple client models to obtain a global model. Remarkably, model averaging in FL yields a …
multiple client models to obtain a global model. Remarkably, model averaging in FL yields a …
Accelerating Federating Learning via In-Network Processing
V Altamore - 2022 - webthesis.biblio.polito.it
The unceasing development of Machine Learning (ML) and the evolution of DeepLearning
have revolutionized many application domains, ranging from natural language processing …
have revolutionized many application domains, ranging from natural language processing …
Modern Autonomous Driving
Y Zhou - 2021 - search.proquest.com
The field of autonomous driving is moving faster than ever, thanks to deep learning (DL)
techniques. As most research focuses on technical perspectives such as perception and …
techniques. As most research focuses on technical perspectives such as perception and …