While relying on verifiable randomness for client selection offers significant advantages in enhancing security and fairness in participant selection processes, there are potential drawbacks or limitations that should be considered:
Complexity: Implementing verifiable randomness mechanisms can introduce additional complexity to FL systems. Clients need to generate random values using cryptographic primitives like VRFs accurately, which may require specialized knowledge or resources.
Computational Overhead: Verifying randomness proofs and conducting consistency checks can add computational overhead to the system, potentially impacting performance during training rounds.
Privacy Concerns: While verifiable randomness helps ensure fair participant selection, it may inadvertently reveal information about clients' behavior or decision-making processes if not implemented carefully.
Scalability Issues: As FL systems scale up with larger populations or more complex algorithms, managing verifiable randomness for all participants could become challenging and resource-intensive.
It is essential for developers implementing such mechanisms to address these drawbacks effectively through careful design considerations and optimizations to mitigate any negative impact on system performance or user experience.