## 2 Related Work  
The field of FL has witnessed substantial attention in recent years [31; 32], driven by the challenges posed by communication between clients in the context of mobile devices. Numerous studies have focused on improving FL performance, addressing issues stemming from the unstable network environment of mobile devices [33]. Problems such as frequent node crashes, dynamic node group shifts, elevated central server overhead, and increased latency with a growing number of nodes have been identified [12; 34]. Additionally, the use of multi-layer models to enhance learning accuracy has been explored, albeit accompanied by challenges related to the increasing number of weights as the layers deepen. The data size constraint in FL due to network transmission between the server and client has also been a prominent concern [35]. Efforts to enhance network performance in FL have involved innovative approaches such as low rank and random mask techniques and temporal weights [36; 37]. However, while aiming to mitigate challenges, these methods may compromise accuracy in unstable network environments. Furthermore, security threats associated with traditional FL models have been identified, emphasizing the potential dangers of transmitting all model weights to the server. Minimizing the collection of weights on the server has emerged as a critical focus to address these security concerns. Simultaneously, the utilization of the Particle Swarm Optimization (PSO) algorithm in distributed environments has gained traction. Various PSO algorithms, including dynamic multi-swarm PSO (DMS-PSO), PSO for neighbor selection in peer-to-peer (P2P) networks, and gossip-based PSO for maintaining flexible P2P networks, have been explored [38; 39]. Studies have demonstrated the adaptability of PSO in diverse distributed environments, showcasing its potential in addressing optimization challenges. [PERSON] et al. [12] identified FL as a method of preserving data privacy by collecting only learned models on a server. The study highlights the challenges of limited communication bandwidth in FL clients, especially in unstable network environments. To address this, the paper introduced the Federated PSO (FedPSO) algorithm, leveraging PSO to transmit score values instead of large weights. FedPSO demonstrates a significant reduction in data used in network communication, leading to an average accuracy improvement of 9.47% and a 4% reduction in accuracy loss on an unstable network. In 2022, two papers introduced new algorithms based on FedPSO, FedGWO [25] and FedSCA [23], extending the exploration of FL techniques. FedGWO reduces data communications by employing the Grey Wolf Optimizer (GWO) algorithm, transmitting score principles rather than all client models' weights. This approach results in a 13.55% average improvement in global model accuracy while decreasing the required data capacity for network communication. Meanwhile, FedSCA proposes a Federated Sine Cosine Algorithm (SCA), utilizing the SCA mechanism for weight updating. FedSCA demonstrates a significant reduction in data used in network communication, leading to an average accuracy improvement of 9.87% over FedAvg and 2.29% over FedPSO, along with a 4.3% improvement in accuracy loss under unstable network conditions. These studies collectively highlight the evolving landscape of FL, showcasing a spectrum of innovative algorithms and techniques to address the unique challenges posed by communication, security, and accuracy in FL environments. The LO has proven highly effective in optimizing tasks across various domains. This section provides an overview of current research on the algorithm, showcasing its applications and advancements. In paddy disease detection, the Modified LO algorithm employsa filter-based feature transformation technique inspired by the Sine Cosine Optimization method [29]. This modification has been demonstrated to improve the accuracy of paddy disease detection through ML techniques, as seen in a study using thermal images of paddy leaves.