Existence and uniqueness of almost periodic solutions for a class of nonlinear Duffing system with time-varying delays

In this paper, we investigate the existence and uniqueness of almost periodic solutions for a class of nonlinear Duffing system with time-varying delays. By using theory of exponential dichotomies and contraction mapping principle, we establish some new results and give an example to illustrate the theoretical analysis in this work

A CONCEPTUAL FRAMEWORK FOR MOBILE GROUP SUPPORT SYSTEMS

The rapid development of wireless communication and mobile devices has created a great opportunity to support mobile group coordination at a more efficient level than before. This article presents a framework for Mobile Group Support Systems (MGSS) that considers four dimensions: supporting whom, supporting what, where to support and how to support. A good MGSS design should take consideration with the characteristics of each dimension: the system should be able to support mobile users working jointly with members from multiple parties; using available and advanced mobile technology, the system should be able to support context freedom, context dependent, and ad hoc coordination under dynamic, uncertain, frequent disrupting, time and space stretched and fluid context. To meet these requirements, we discuss the issues related to three basic functions of MGSS: mobile communication, group coordination, and context awareness

赤身魚肉のミオグロビン還元機構に関する研究

Tohoku University落合芳博課

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.