Explore the vast range of titles available.

In this manuscript, we develop a multi-party framework tailored for multiple data contributors seeking machine learning insights from combined data sources. Grounded in statistical learning principles, we introduce the Multi-Key Homomorphic Encryption Logistic Regression (MK-HELR) algorithm, designed to execute logistic regression on encrypted multi-party data. Given that models built on aggregated datasets often demonstrate superior generalization capabilities, our approach offers data contributors the collective strength of shared data while ensuring their original data remains private due to encryption. Apart from facilitating logistic regression on combined encrypted data from diverse sources, this algorithm creates a collaborative learning environment with dynamic membership. Notably, it can seamlessly incorporate new participants during the learning process, addressing the key limitation of prior methods that demanded a predetermined number of contributors to be set before the learning process begins. This flexibility is crucial in real-world scenarios, accommodating varying data contribution timelines and unanticipated fluctuations in participant numbers, due to additions and departures. Using the AI4I public predictive maintenance dataset, we demonstrate the MK-HELR algorithm, setting the stage for further research in secure, dynamic, and collaborative multi-party learning scenarios.

Four different interaction styles for the social robot Furhat acting as a host in spoken conversation practice with two simultaneous language learners have been developed, based on interaction styles of human moderators of language cafés. We first investigated, through a survey and recorded sessions of three-party language café style conversations, how the interaction styles of human moderators are influenced by different factors (e.g., the participants language level and familiarity). Using this knowledge, four distinct interaction styles were developed for the robot: sequentially asking one participant questions at the time (Interviewer); the robot speaking about itself, robots and Sweden or asking quiz questions about Sweden (Narrator); attempting to make the participants talk with each other (Facilitator); and trying to establish a three-party robot–learner–learner interaction with equal participation (Interlocutor). A user study with 32 participants, conversing in pairs with the robot, was carried out to investigate how the post-session ratings of the robot’s behavior along different dimensions (e.g., the robot’s conversational skills and friendliness, the value of practice) are influenced by the robot’s interaction style and participant variables (e.g., level in the target language, gender, origin). The general findings were that Interviewer received the highest mean rating, but that different factors influenced the ratings substantially, indicating that the preference of individual participants needs to be anticipated in order to improve learner satisfaction with the practice. We conclude with a list of recommendations for robot-hosted conversation practice in a second language.

The demand for high-quality private data in large language models is growing significantly. However, private data is often scattered across different entities, leading to significant data silo issues. To alleviate such problems, we propose a novel multi-party collaborative training framework for large language models, named MPCTF. MPCTF consists of several components to achieve multi-party collaborative training: (1) a one-click launch mechanism with multi-node and multi-GPU training capabilities, significantly simplifying user operations while enhancing automation and optimizing the collaborative training workflow; (2) four data partitioning strategies for splitting client datasets during the training process, namely fixed-size strategy, percentage-based strategy, maximum data volume strategy, and total data volume and available GPU memory strategy; (3) multiple aggregation strategies; and (4) multiple privacy protection strategies to achieve privacy protection. We conducted extensive experiments to validate the effectiveness of the proposed MPCTF. The experimental results demonstrate that the proposed MPCTF achieves superior performance; for example, our MPCTF acquired an accuracy rate of 65.43 and outperformed the existing work, which acquired an accuracy rate of 14.25 in the experiments. Moreover, we hope that our proposed MPCTF can promote the development of collaborative training for large language models.

Computational ghost imaging (CGI) is an imaging technique that uses the second-order correlation properties of optical fields. It uses random illumination patterns to illuminate the object, collects the signal via a single-pixel bucket detector, and later reconstructs an image of the object. By treating the illumination patterns as keys and the bucket measurements as ciphertexts, CGI can be regarded as an encryption process. This paper proposes a multi-party collaborative decryption scheme based on CGI, which aims to reduce key transmission data whilst enabling cooperative decryption among multiple users and verifying the legitimacy of the ciphertext source via digital watermarking. This method uses a chaotic system to generate a measurement matrix, and only the chaotic parameter needs transmission. The receiver can reconstruct the measurement matrix, thereby reducing the amount of key transmission. Further, verifiable secret sharing (VSS) is employed to divide the chaotic parameter into multiple shares, which are then distributed to different recipients. Decryption is only feasible when a threshold number of recipients collaborate to reconstruct the chaotic parameter. To further improve security, hashed identity information is embedded into the ciphertext as a watermark, enabling confirmation of the ciphertext’s origin. Experimental results show the feasibility of the proposed scheme and its ability to resist tampering, offering a new approach to secure multi-user communication.