## 2 Federated Learning Approaches
**Federated clustering framework:** [PERSON] and [PERSON] [5] presented for first time a federated clustering framework that addresses three key challenges: (i) determining the optimal number of global clusters within a federated dataset, (ii) creating a data partition using a federated fuzzy c-means algorithm, and (iii) validating the quality of the clustering using a federated fuzzy Davies-Bouldin index. This framework is thoroughly evaluated through numerical experiments conducted on both artificial and real-world datasets. The findings indicate that, in most instances, the results achieved by the federated clustering framework are in line with those of the non-federated counterpart. Additionally, the authors incorporate an alternative federated fuzzy c-means formulation into the proposedframework and observe that this approach exhibits greater reliability when dealing with non-IID data, while performing equally well in the IID scenario. **Blockchain-based incentive mechanism:** In response to the issues associated with centralization and the absence of incentives in conventional federated learning, [PERSON] et al. [8] introduced a decentralized federated learning method for edge computing environments based on blockchain technology, called BD-FL. This approach integrates all edge servers into the blockchain network, where the edge server nodes that acquire bookkeeping rights collectively aggregate the global model. This addresses the centralization problem inherent in federated learning, which is prone to single points of failure. To encourage active participation of local devices in federated learning model training, an incentive mechanism is introduced. To minimize the cost of model training, BD-FL employs a preference-based stable matching algorithm which associates local devices with suitable edge servers, effectively reducing communication overhead. Furthermore, the authors propose the utilization of a reputation-based practical Byzantine fault tolerance (R-PBFT) algorithm to enhance the consensus process for global model training within the blockchain. The experimental results demonstrate that BD-FL achieves a notable reduction in model training time of up to 34.9% compared to baseline federated learning methods. Additionally, the incorporation of the R-PBFT algorithm enhances the training efficiency of BD-FL by 12.2%. **Influence of unreliable participants and selective aggregation:** [PERSON] et al. [9] introduced a multi-mobile Android-based implementation of a federated learning system and investigated how the presence of unreliable participants and selective aggregation influence the feasibility of deploying mobile federated learning solutions in real-world scenarios. Furthermore, the authors illustrated that employing a more sophisticated aggregation method, such as a weighted average instead of a simple arithmetic average, can lead to substantial enhancements in the overall performance of a federated learning solution. In the context of ongoing challenges in the field of federated learning, this research argues that imposing eligibility criteria can prove beneficial to the context of federated learning, particularly when dealing with unreliable participants. **Survey on privacy and security in FL:** [PERSON] et al. [2] presented a comprehensive survey addressing various privacy and security issues related to federated learning (FL). Toward this end, the authors introduce an overview of the FL applications, network topology, and aggregation methods, and then present and discuss the existing FL-based studies in terms of security and privacy protection techniques aimed at mitigating FL vulnerabilities. The findings show that the current major security threats are poisoning, backdoor, and Generative Adversarial Network (GAN) attacks, while inference-based attacks are the most critical for FL privacy. Finally, this study concludes with FL open issues and provides future research directions on the topic.