Security

The FLock system is designed to be resilient against a wide array of potential attacks, ensuring the integrity and security of its operations.

For instance, Sybil attacks are mitigated by requiring participants to stake a minimum amount of assets, making it costly to control multiple identities. Furthermore, validators are kept unaware of the model origins, reducing the risk of collusion, and only the top-performing training nodes and validators receive rewards, discouraging poor performance and manipulation.

To mitigate DoS attacks, the Flock system implements rate limiting, preventing any single participant from monopolising resources. Free-rider attacks are addressed by rewarding only the top contributors, ensuring that participants who do not genuinely contribute cannot benefit.

The use of dual datasets (Dataset A and B) in evaluations prevents lookup attacks, as optimizing for one dataset does not guarantee success in the other.

For FL model poisoning attacks, a majority voting system and slashing mechanism protect the model's integrity, punishing malicious actors and discouraging future attempts. These measures collectively fortify the FLock system against a range of threats, promoting a secure and reliable environment for participants.