# RingFFL: A Ring-Architecture-Based Fair Federated Learning Framework
## 2 Related Works
### Distributed FL Framework  
The distributed FL framework based on fully connected architecture completes model training through P2P model transmission between clients, and the implementation of the architecture often requires the support of blockchain, Device-to-Device (D2D), and so on [17; 18; 19]. [PERSON] et al. proposed a novel method based on distributed FL to estimate the tail distribution of vehicle formation lengths, approaching the accuracy of the centralized solution while reducing the amount of data exchanged by 79% [17]. [PERSON] et al. proposed a decentralized blockchain-based FL framework called the Blockchain-based Federated Learning framework with Committee consensus (BFLC), which avoided attacks by malicious central servers on global models and users' private data, and designed an innovative committee consensus mechanism for BFLC that could effectively reduce consensus computation and malicious attacks [18]. [PERSON] et al. proposed a novel blockchain-based FL scheme called FL-Block that balanced the privacy protection of fog computing with the attendant inefficiencies, excelling in privacy protection, efficiency, and resistance to poisoning attacks [36]. [PERSON] et al. proposed distributed stochastic gradient descent (DSGD) to achieve large-scale deployment of FL in wireless communication scenarios [37]. [PERSON] et al. proposed an FL scheme using D2D communication (D2D-FedAvg), where D2D grouping, primary UE selection, and D2D exit were implemented in the learning process, resulting in a complete D2D-assisted FL averaging algorithm [19]. Although the problem of poor scalability and vulnerability to single-point attacks of star architectures is solved to some extent by using fully connected architecture, it brings a lot of redundant communication consumption, and it is difficult to guarantee the convergence of the model due to the lack of global aggregation.  
Some scholars have designed a ring architecture to complete the training through the ring transmission of models while ensuring lower communication redundancy and higher convergence [20; 21; 22; 23]. [PERSON] et al. proposed an optimization method to minimize FL uplink transmission time in wireless networks and designed a ring all-reduce architecture based on D2D, which significantly reduced the transmission time [20]. [PERSON] et al. proposed a ring-topology-based distributed FL (RDFL) scheme for deep generative modeling (DGM), which provided communication efficiency and maintained training performance to boost DGMs in target tasks compared with existing FL works [21]. [PERSON] et al. designed a cyclic learning scheme based on the ring topology to reduce the number of FL training iterations and improve the performance without adding any additional computational cost [22]. [PERSON] et al. proposed a new algorithm called TornadoAggregate to improve FL accuracy and scalability by promoting a ring architecture. Experimental results show that TornadoAggregate improved test accuracy by 26.7% and achieved near-linear scalability [23].  
The above schemes usually assume that the clients are honest. However, the assumption is difficult to be satisfied in realistic situations. A malicious client sending escape attacks during the training process can be fatal to FL based on the ring architecture. Therefore, we propose a fair FL framework to solve the problem. With the proposed penalties mechanism, clients are effectively prevented from escaping from the training after obtaining valid information.