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This paper studies a federated learning (FL) system, where multiple FL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprising intra-service resource allocation and inter-service resource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Based on this, an inter-service resource allocation problem is further considered, which distributes bandwidth resources among multiple simultaneous FL services. We consider both cooperative and selfish providers of the FL services. For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients. For selfish FL service providers, a new auction scheme is designed with the FL service owners as the bidders and the network provider as the auctioneer. The designed auction scheme strikes a balance between the overall FL performance and fairness. Our simulation results show that the proposed algorithms outperform other benchmarks under various network conditions.

Teaching ability evaluation of professional teachers is an important means of teaching management to the related teaching department, and it plays a very important role in enhancing the teaching ability of teachers and the quality of teaching. In this paper, by analyzing the disadvantages of traditional evaluation methods, an evaluation model of university teachers' teaching ability is put forward based on the fuzzy comprehensive evaluation, and the evaluation model of the specific composition and the corresponding algorithm are also proposed. The model is better than common evaluation tools to weigh the various factors in the case of making information not lose. It can naturally deal with the initiative and fuzziness of the human mind, so it makes the every teacher evaluated achieve an objective and fair evaluation result.