Blockchain Layer
This section describes how the blockchain layer of FLock enhances both incentivisation and security.
Incentivisation
The blockchain layer plays a pivotal role in reward distribution. It ensures that participants can securely lock in their stakes, fostering an environment of trust and transparency. The process is designed to incentivise participation by allocating rewards based on contributions, thus encouraging a more engaged and active community. Through the use of smart contracts, the system automates the rewards process, making it both efficient and fair. This automation not only reduces the potential for human error but also ensures that rewards are distributed in a timely and fair manner.
Security
At FLock, we propose a generic framework that can integrate an FL system with a blockchain system and can defend against poisoning attacks without adopting complex cryptographic protocols, which is adopted from our peer-reviewed work here.
Specifically, FLock employs a proof-of-stake mechanism with the goal of ensuring robust security and consensus within the network. Such system enhances Byzantine fault tolerance by aligning participants' incentives with network integrity— those who act dishonestly or fail to reach consensus risk losing their staked tokens. This economic disincentive promotes honest behaviour, ensuring the network remains secure and reliable.
The table below describes how FLock mitiagtes vairous types of risks which could occur in decentralised training platforms.
Attacks | Description | FLock Mitigation |
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Sybil Attacks | An attacker might gain disproportionate influence in the FLock system by creating and controlling multiple fake identities of participants. |
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DoS Attacks | An attacker might exhaust the FLock system resource and make it unavailable to honest participants. |
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Free-rider Attacks | Free riders benefit from a system without contributing fairly. In the FLock system, a free rider training node may randomly submit models without actuall training. Similarly, free rider validators give random scores instead of honestly evaluating models. |
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Lookup Attacks | Training nodes could cheat by learning to predict past validation score calculations. |
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FL Model Poisoning Attacks | In FL Alliance, an attacker may use biased or corrupted data during the training process to degrade the model's performance. |
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