Huang Zeng, Mingtian Zhang, Tengfei Liu, Anjia Yang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5125-5142, 2024, DOI:10.32604/cmc.2024.052846 - 20 June 2024

Abstract Federated learning is an important distributed model training technique in Internet of Things (IoT), in which participant selection is a key component that plays a role in improving training efficiency and model accuracy. This module enables a central server to select a subset of participants to perform model training based on data and device information. By doing so, selected participants are rewarded and actively perform model training, while participants that are detrimental to training efficiency and model accuracy are excluded. However, in practice, participants may suspect that the central server may have miscalculated and thus… More >

Byeori Kim¹, Minseong Choi¹, Taek-Young Youn², Jeong Hyun Yi¹, Haehyun Cho^1,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3725-3742, 2023, DOI:10.32604/iasc.2023.036574 - 15 March 2023

Abstract Recently, there has been a sudden shift from using traditional office applications to the collaborative cloud-based office suite such as Microsoft Office 365. Such cloud-based systems allow users to work together on the same document stored in a cloud server at once, by which users can effectively collaborate with each other. However, there are security concerns unsolved in using cloud collaboration. One of the major concerns is the security of data stored in cloud servers, which comes from the fact that data that multiple users are working together cannot be stored in encrypted form because More >