## 2 Related Work  
FL is a collaborative approach that enhances data privacy by allowing multiple organizations to train models without sharing raw data. Ensuring high-quality data contributions is critical for robust model performance. [PERSON] et al. [10] and [PERSON] et al. [11] focus on improving model accuracy by prioritizing clients with high-quality data and using evaluation metrics. [PERSON] et al. [12] and [PERSON] et al. [13] introduce dynamic participation and continuously monitor the data quality to optimize learning processes. Lastly, [PERSON] et al. [14] described Trustworthy Federated Learning as a Service (TruFLaaS), a trustworthy federated learning service. TruFLaaS emphasized the importance of robust security and trust mechanisms to ensure data privacy and security in the federated learning process.  
Considering the security threats posed by unauthorized access to this data, we need authentication mechanisms to ensure the secure transmission of data in the FL scenario. Since the first introduction of a 2 FA protocol, a plethora of subsequent protocols have been designed to improve upon the deficiencies of earlier designs. [PERSON] et al. [6] introduced an innovative anonymous authentication protocol tailored for IoT devices using ECC combined with signcryption. This protocol significantly reduces computational and communication overheads, but it fails to fully secure user anonymity, resulting in potential vulnerabilities for user activity tracking and device traceability.  
[PERSON] et al. [7] developed a 2 FA protocol that uses ECC to enhance security in mobile computing. While it effectively authenticates users and aggregated servers, it remains susceptible to Denial of Service (DoS) attacks and does not adequately address ephemeral session linkability (ESL) attacks within the extended Canetti-Krawczyk (eCK) security model.  
In the healthcare scenario, [PERSON] et al. [15] developed a secure authentication protocol using Kyber and AES-GCM encryption amidst the digital health records transition accelerated by the pandemic. This protocol enables secure transactions without physical key exchanges and generates unique session keys. [PERSON] et al. [16] presented a mutual authentication protocol for tele-surgery, utilizing ECC and biometrics to secure data transmission between surgeons and robotic arms. However, both protocols exhibit performance drawbacks, particularly in computational efficiency, which is critical in healthcare applications.  
In the domain of wearable and mobile computing, [PERSON] et al. [17] designed an EAKE-WC protocol for wearable computing that tackles privacy and security issues using ASCON [18], XOR operations, and hashing for user and device authentication, ensuring secure communication through unique session keys. [PERSON] et al. [8] proposed an ultra-lightweight authentication method for mobile edge computing, protecting patient biomedical data in IoT-driven WBANs with a two-stage modal square root technique. However, this method, while reducing computational and communication costs, fails to ensure user anonymity and device un-traceability.  
Furthermore, [PERSON] et al. [19] tackled the challenge of ensuring item traceability within industrial IoT (IIoT), introducing a robust auditing mechanism capable of withstanding manipulations by malicious cloud servers. This mechanism represents a broader trend towards employing sophisticated auditing techniques to enhance data integrity in IIoT.  
Additionally, in the IoT-Edge-Cloud ecosystem, [PERSON] et al. [20] unveiled Mutual Authentication Privacy-preserving protocol with Forward Secrecy (MAPFS), a mutual authentication protocol that eliminates reliance on cloud administrators for session key establishment and incorporates zero-knowledge proofs to enhance security and autonomy.