In recent years, the explosion of big data has presented unparalleled opportunities for the advancement of machine learning (ML). However, the vast size and sensitive nature of these datasets present significant challenges in terms of privacy and security. Federated Learning has emerged as a promising solution that enables a group of participants to train ML models without compromising the confidentiality of their raw data. Despite its potential, traditional federated learning faces challenges such as the absence of participant incentives and audit mechanisms. Furthermore, these challenges become more significant when dealing with the scale and diversity of big data, making efficient and reliable federated learning a complex task. These limitations may compromise model quality due to potential malicious nodes. To address the above issues, this paper proposes a BlockChain-based Decentralized Federated Learning (BCD-FL) model. In BCD-FL, we design a smart contract approach based on the reverse auction-based incentive mechanism and a reputation mechanism to promote the participation of reliable and high-quality data owners. Theoretical analysis shows that the BCD-FL model satisfies several desirable properties, such as individual rationality, computational efficiency, budget balance, and truthfulness. In addition, experimental results also show that the proposed model enables more efficient federated learning and provides some level of protection against malicious nodes. Therefore, the BCD-FL model presents a potential solution to the challenges in federated learning and opens up new possibilities for achieving efficient large-scale machine learning.

Distributed data storage can assist compressive crowdsensing to solve the challenge of temporary data storage in the network. Block compressive sensing effectively addresses distributed data storage of large spatiotemporal data from crowdsensing, allowing efficient reconstruction at low storage and computational costs. However, when the data is unevenly distributed over the sensing area, existing algorithms ignore the variability of information between blocks and still require the central server to uniformly collect samples from each block stored on the mobile device, leading to a reduction in overall reconstruction accuracy. To address this, we propose an adaptive sampling allocation strategy that deeply analyzes the statistical information of each block which can help the central server to collect the number of measurements for each block adaptively to improve the sampling quality. Additionally, we consider the correlation between blocks and use a global denoising strategy to further improve the reconstruction accuracy. Experimental results demonstrate that, compared to the state-of-the-art DDS-CCS algorithm, our proposed adaptive sampling allocation with a joint-denoising mechanism significantly improves the accuracy of the information-rich blocks that most affect the global accuracy, and hence the global reconstruction accuracy. which also remains robust to different block sizes and exhibits improved stability.

Identifying synergistic drug combinations is a critical but difficult challenge in cancer treatment, owing to the sheer complexity and enormous number of possible drug combinations. However, most existing computational methods rely on a single data perspective and often overlooking the complexity of interactions between different biological entities. Furthermore, they fail to fully integrate the intrinsic properties of drugs and cell lines with the broader biological relationships that play a crucial role in drug synergy. To address these challenges, we propose a novel framework called LGSyn that integrates two types of information: local features, including molecular fingerprints, descriptors, and gene expression profiles, as well as global features that encompass broader biological interactions, including drug-protein, protein-cell line, protein-protein, and cell line-tissue interactions. By combining these two types of features, LGSyn leverages the full spectrum of biological knowledge to predict drug synergy. In LGSyn, we developed three fusion strategies to effectively integrate local and global information and identify the most suitable strategy. The resulting fused feature vectors are then fed into a deep neural network for training and synergy prediction. Experimental results demonstrate that the proposed method outperforms current state-of-the-art models, achieving superior accuracy and stability in drug synergy prediction. The source code of LGSyn is publicly available at https://github.com/1zuoying/LGSyn.