Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios

Federated Learning (FL) is a promising technology that enables multiple actors to build a joint model without sharing their raw data. The distributed nature makes FL vulnerable to various poisoning attacks, including model poisoning attacks and data poisoning attacks. Today, many byzantine-resilient FL methods have been introduced to mitigate the model poisoning attack, while the effectiveness when defending against data poisoning attacks still remains unclear. In this paper, we focus on the most representative data poisoning attack - "label flipping attack" and monitor its effectiveness when attacking the existing FL methods. The results show that the existing FL methods perform similarly in Independent and identically distributed (IID) settings but fail to maintain the model robustness in Non-IID settings. To mitigate the weaknesses of existing FL methods in Non-IID scenarios, we introduce the Honest Score Client Selection (HSCS) scheme and the corresponding HSCSFL framework. In the HSCSFL, The server collects a clean dataset for evaluation. Under each iteration, the server collects the gradients from clients and then perform HSCS to select aggregation candidates. The server first evaluates the performance of each class of the global model and generates the corresponding risk vector to indicate which class could be potentially attacked. Similarly, the server evaluates the client's model and records the performance of each class as the accuracy vector. The dot product of each client's accuracy vector and global risk vector is generated as the client's host score; only the top p\% host score clients are included in the following aggregation. Finally, server aggregates the gradients and uses the outcome to update the global model. The comprehensive experimental results show our HSCSFL effectively enhances the FL robustness and defends against the "label flipping attack.

PM Eddy Current Loss Analysis and Suppression in a Spoke-Type Flux-Modulation Machine With Magnetic Flux Barrier Design

This paper investigates the permanent-magnet (PM) eddy current (EC) loss in a spoke-type flux-modulation (STFM) machine with inverted T-shaped permanent magnets. The key is to design appropriate flux barriers in such rotor structures to suppress the PM EC loss with little effect on torque performance. First, the main harmonic orders generating PM EC loss are analyzed based on the air-gap magnetic field modulation. The findings indicate that some non-working harmonics produce PM EC loss, which can be suppressed by reducing these harmonics. And the proportion of PM EC loss produced by these harmonics is calculated through the analysis of a three-dimensional model of PM EC loss. Then, a new rotor structure with flux barriers is proposed to suppress PM EC loss based on ensuring the torque performance by reducing the 1st-order harmonic. Finally, the electromagnetic performance of the two machines is compared by finite element analysis. Stress analysis and temperature distribution simulation are also carried out to prove the feasibility of this structure. The results show that the proposed structure is effective in suppressing PM EC loss with little influence on the main electromagnetic performance

MiR-378a suppresses tenogenic differentiation and tendon repair by targeting at TGF-β2

Background: Tendons are a crucial component of the musculoskeletal system and responsible for transmission forces derived from muscle to bone. Patients with tendon injuries are often observed with decreased collagen production and matrix degeneration, and healing of tendon injuries remains a challenge as a result of limited understanding of tendon biology. Recent studies highlight the contribution of miR-378a on the regulation gene expression during tendon differentiation. Methods: We examined the tendon microstructure and tendon repair with using miR-378a knock-in transgenic mice, and the tendon-derived stem cells were also isolated from transgenic mice to study their tenogenic differentiation ability. Meanwhile, the expression levels of tenogenic markers were also examined in mouse tendon-derived stem cells transfected with miR-378a mimics during tenogenic differentiation. With using online prediction software and luciferase reporter assay, the binding target of miR-378a was also studied. Results: Our results indicated miR-378a impairs tenogenic differentiation and tendon repair by inhibition collagen and extracellular matrix production both in vitro and in vivo. We also demonstrated that miR-378a exert its inhibitory role during tenogenic differentiation through binding at TGFβ2 by luciferase reporter assay and western blot. Conclusions: Our investigation suggests that miR-378a could be considered as a new potential biomarker for tendon injury diagnosis or drug target for a possible therapeutic approach in future clinical practice