# A Fairness-Aware Peer-to-Peer Decentralized Learning Framework with Heterogeneous Devices
## 2 Related Work  
We now review the existing research efforts related to our study. While federated learning has received great attention recently, there are a number of open issues about its deployment in real-world scenarios, such as (i) data distribution (e.g., non-i.i.d., unbalanced data, and learning fairness issues); (ii) system security and reliability (e.g., security threats, communication efficiency, and others); and (ii) learning structures (e.g., centralized, federated, decentralized). Next, we summarize the related works from these three aspects, respectively. To address the learning fairness issues in a heterogeneous environment, [PERSON] et al. [7] proposed a federated learning framework with a fair resource allocation. A fairness metric (\(q\)-Fair coefficient) is parameterized by the variance of learning performance. Via the tuning \(q\)-Fair coefficient, the performance distribution of the proposed learning framework shows a balanced learning accuracy in the local training group. [PERSON] et al. [8] proposed a multiplayer game under federated learning to study the action selection of federated learning participants in which various incentive mechanisms are involved. [PERSON] et al. [9] studied the collaborative fairness issue and proposed a reputation-based learning approach to release diverse models over the local training group. Moreover, [PERSON] et al. [10] investigated the fairness issues (i.e., performance distribution) and robustness (i.e., label poisoning, random updates, and model replacement) in the federated learning process. Likewise, [PERSON] et al. [10] proposed a lightweight multitask learning approach to tackle the competing constraints of learning performance, fairness, and robustness in federated learning. Some existing research works aim to solve the learning fairness problem of large-scale personalized federated learning [11; 12]. For instance, [PERSON] et al. [12] proposed a learning approach, namely FedFomo, to achieve the model personalization. They designed a scheme to obtain an approximation of the optimal weight of a model combination for each local learner. Nonetheless, most of the aforementioned works have to deploy the proposed scheme with a central parameter server, which brings the extra computing cost and security concerns (e.g., single-point failure issue) to the learning process. Additionally, an unexpected backdoor would be spread to all of the local clients if the malicious updates can avoid the detection of a detection mechanism on the parameter server. To consider the learning system security and reliability, [PERSON] et al. [13] proposed a game-theoretic approach in federated learning to investigate the node behavior with various scenarios. The proposed learning scheme takes incentive-aware collaboration into account. [PERSON] et al. [14] introduced the blockchain technology into the federated learning process to address the potential security issues, namely the blockchain-based federated learning approach with committee consensus (BFLC). The proposed approach records the global models and the local model updates with the evaluation of the committee consensus mechanism. A trusted committee could be elected from three different methods, including random election, election by score, and multifactor optimization. Moreover, [PERSON] et al. [4] proposed a zero-knowledge clustering-based scheme to mitigate adversarial attacks in federated learning environments. The proposed scheme is capable of not only alleviating the multiparty model poisoning attack during the training process but also allowing the central parameters server to automatically modify the number of clusters in the detection stage. Decentralized learning has been considered a promising paradigm to provide the flexibility over heterogeneous learning environments. Related to this direction, [PERSON] et al. [15] investigated the performance gap between centralized learning and decentralized learning.