This work investigates the update security model in a collaborative learning or federatedlearning network by using the covert communication. The covert communication (CC) uses the jamming signal and multiple friendly jammers (FJS) are deployed that can offer jamming services to the model owner, i.e., a base station (BS). To enable the BS to select the best FJ, i.e., the lowest cost FJ, a truthful auction is adopted. Then, we formulate an optimization problem that aims to optimize the transmission power, jamming power, and local accuracy. The objective is to minimize the training latency, subject to the security performance requirement and budget of the BS. To solve the non-convex problem, we adopt a Successive Convex Approximation algorithm. The numerical results reveal some interesting things. For example, the trustful auction reduces the jamming cost of the BS as the number of FJs increases.

Following our investigations on particular fuzzy algebraic structures. we reaisit fuzzysubgroups and fuzzy ideals and introduce some numerical examples. As usual. we associated relationsto fuzzy subgroup and fuzzy ideal. Consequently, right and left cosets modulo a fuzzy relation were introduced. This work and the our previous works can be considered as a continuation of irrestigaiions initiated by Abu Osman and Antony in the 19805. Toward our investigation, we have in mind that by introducing these new definitions, the results that we can get should represent a real generalization of classical and commonly known concepts of algebra

In this work, we consider the Dirichlet boundary value problem for nonlinear Tihmonic equation. Due to the reduction of the problem to operator equation for the pair of tr: right hand function and the unknown second normal derivative of the function to be we design iterative method at both continuous and discrete levels for numerical solution problem. Some examples demonstrate that the numerical method is of fourth order convergeze. “hen the hand side function does not depend on the unknown function and its deritatives- numerical method gives more accurate results in comparison with the results obtained by the firefly: method Gudi and Neilan.

This paper is to present a novel model of estimating trustworthiness 6 a truster on a trustee based on experience trust and reputation trust from some community xvii: 131- context of user‘s topic interests. Firstly, we construct a measure of experience topic-21mm:-which isdefined as a function of degrees of interaction from a truster to some trustee and a ties-interests in topics. Secondly, we construct a measure of reliability degree of conic-.137: on some trustee by means of a function which is computed via degrees of reliability of truster members of the community and similarity of these members with the trustee. Thirdly. we propose a (imposition function for estimating an overal topic-aware trust based on experience topic—aware trust and the reputation topic-aware trust. Our experimental results show that the degree of experience topic- aware trust depends on interaction degree among truster and trustee more than on trustee‘s interest degree. They also indicate that the overall topic-aware trust estimation depends on reputation from community more than user’s own experience evaluation.

Instead of handling fuzzy sets associated with linguistic (L-) laltmis asset the devel—opers’ intuition immediately, the study follows the hedge algebras (HA-) approach to Lie- time seriesforecasting problems, in which the linguistic time series forecasting model was. for the first time.proposed and examined in 2020. It can handle the declared forecasting L—variabie word-set directly and, hence, the terminology linguistic time-series (LTS) is used instead of the fuzzy time-series (FTS). Instead of utilizing a limited number of fuzzy sets, this study views the L-variable under considera - tion as to the numeric forecasting variable’s human linguistic counterpart. Hence. its word—domain becomes potentially infinite to positively utilize the HA-approach formalism for increasing the LTS forecasting result exactness. Because the forecasting model proposed in this study can directly handle L—words. the LTS, constructed from the numeric time series and its L-relationship groups. Considered human knowledges of the given time—series variation helpful for the human-machine interface. The study shows that the proposed formalism can more easily handle the LTS forecasting models and increase their performance compared to the FTS forecasting models when the words‘ munher grows.