# RingFFL: A Ring-Architecture-Based Fair Federated Learning Framework
###### Abstract  
In the ring-architecture-based federated learning framework, security and fairness are severely compromised when dishonest clients abort the training process after obtaining useful information. To solve the problem, we propose a **Ring-** architecture-based **Fair Federated Learning framework called RingFFL**, in which we design a penalty mechanism for FL. Before the training starts in each round, all clients that will participate in the training pay deposits in a set order and record the transactions on the blockchain to ensure that they are not tampered with. Subsequently, the clients perform the FL training process, and the correctness of the models transmitted by the clients is guaranteed by the HASH algorithm during the training process. When all clients perform honestly, each client can obtain the final model, and the number of digital currencies in each client's wallet is kept constant; otherwise, the deposits of clients who leave halfway will be compensated to the clients who perform honestly during the training process. In this way, through the penalty mechanism, all clients either obtain the final model or are compensated, thus ensuring the fairness of federated learning. The security analysis and experimental results show that RingFFL not only guarantees the accuracy and security of the federated learning model but also guarantees the fairness. federated learning; fairness; blockchain; ring architecture +
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and ring architectures [20; 21; 22; 23]. Under the fully connected architecture, clients form a P2P network, in which each client transmits local model parameters to neighboring clients, receives local model parameters transmitted from neighbors to complete model aggregation, and continues to transmit until the models finally converge. However, this approach brings a large amount of redundant communication, and it is difficult to guarantee the convergence of the model because each client performs aggregation locally [24]. In the ring architecture, clients are connected in a ring communication network in a certain order, and during the training process, each client only needs to transmit the local model parameters to the next client. The next client will either continue training based on the model received or transmit to the next client after aggregating it with its local model. Through multiple rounds of loop transmission, the converged global model is finally obtained. The ring structure achieves better results due to its more efficient communication efficiency and better convergence. In the above scenario, it is usually assumed that all clients are honest, that is, all clients participate in the training process in its entirety [25]. In fact, a client may maliciously abandon the subsequent training after obtaining a result in their favor or leave midway unintentionally for technical reasons such as unstable network conditions, and the impact of such dishonest behavior (called escape attack) on the ring structure is catastrophic [26]. Firstly, the model training may be unsustainable due to a break in the ring communication at a certain point, which will lead to low accuracy of FL. Secondly, the intermediate result obtained by the dishonest client contains data contributions from honest clients, but honest clients did not obtain any benefit, which is unfair to them. Therefore, the escape attack needs to be avoided as much as possible to ensure the fairness of all clients participating in the model training. Motivated by the above analysis, we propose a **Ring**-architecture-based **Fair** Federated Learning framework (RingFFL), in which we design a penalty mechanism to guarantee the fairness of FL, that is, the client either obtains the model that is transmitted correctly or is compensated with a certain amount of digital currency. The HASH comparison guarantees the correctness of the models transmitted during the training process.