Highlights d Cities possess a consistent ''core'' set of non-human microbes d Urban microbiomes echo important features of cities and city-life d Antimicrobial resistance genes are widespread in cities d Cities contain many novel bacterial and viral species

Multi-Issue Negotiation protocols have been studied very widely and represent a promising field since most of negotiation problems in the realworld are complex ones including multiple issues. In particular, in reality issues are constrained each other. This makes agents' utilities nonlinear. There have been a lot of work on multi-issue negotiations. However, there have been very few work that focus on nonlinear utility spaces. In this paper, we assume agents have nonlinear utility spaces. For the linear utility domain, agents can aggregate the utilities of the issue-values by simple linear summation. In the real world, such aggregations are unrealistic. For example, we cannot just add up the value of car's tires and the value of car's engine when engineers design a car. In this paper, we propose an auction-based multiple-issue negotiation protocol among nonlinear util-1 ity agents. Our negotiation protocol employs several techniques, i.e., adjusting sampling, auction-based maximization of social welfare. Our experimental results show that our method can outperform the existing simple methods in particular in the huge utility space that can be often found in the real-world. Further, theoretically, our negotiation protocol can guarantee the completeness if some conditions are satisfied.

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Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.