## 3 Research Questions and Communication Efficient Methods
### RQ2-How Can Communication Be More Efficient in an FL Environment?
#### 3.2.4 Decentralized Training and Peer-to-Peer Learning  
FL, in a way, is like practicing ML in a decentralized manner. However, FL does allow for a more peer-to-peer learning approach wherein each trained node can benefit from the other node that is trained on the FL network. Even in decentralized training, similar challenges of communication exist, and, in order to tackle it, different methods such as compression [77] can be used. Herein, this subsection presents how decentralized or peer-to-peer learning is utilized or integrated into an FL environment. In an FL environment, as aforementioned, there is a central server that has the original model. The central server is also where all of the devices that are connected to the network update their model. Essentially, all devices that are participating in the FL environment are connected to the central server. As stated by [48], the star network is the predominant communication topology in an FL setting as shown in Figure 7. However, there is some research on a different communication topology, where the participating devices only communicate with one another, a peer-to-peer learning experience or decentralized training network, and whether this would be a more efficient way to communicate in an FL environment. The authors in [48] state that, in traditional data center environments, decentralized training can appear to be faster than centralized training, especially when constraints of a low bandwidth and high latency are faced when operating on networks. However, this is not to say that decentralized training does not have its constraints: the authors in [78] state that the computation time on nodes can slow down the convergence of a decentralized algorithm. In addition to this, sometimes, a large communication overhead could also further mitigate this. To overcome these constraints, the authors in [78] proposed the QuanTimed-DSGD algorithm, a decentralized and gradient-based optimization that imposes iteration deadlines for nodes, where nodes exchange their quantized version of the models. In the FL environment, peer-to-peer learning could be used, where each device only communicates with its neighbours and updates. The participating devices or clients update their model on their dataset and aggregate it along with the model updates from their neighbours [39]. Furthering that, the authors in [79] build a framework for an FL environment towards a generic social network scenario. Their online push-sum (OPS) method handles the complex topology while simultaneously having optimal convergence rates. Similarly, [80] proposes a distributed learning algorithm where there is no central server but, instead, the participating devices practice a peer-to-peer learning algorithm to iterate and aggregate model information with their neighbor to estimate the global model parameters collaboratively. The authors put forward an assumption, suggesting that the FL setting wherein a central server exists and communicates with the global model can incur high communication costs. In their approach, the devices are already distributed over the network, where communication occurs only with their one-hop neighbors. The authors in [81] further provide another avenue for peer-to-peer learning that does not depend on the central server as a single trusting authority. The authors [81] create a framework, BrainTorrent, that does not rely on a central server for training; their proposed peer-to-peer framework is designed to motivate a collaborative environment. According to their analysis, the absence of a central server makes the environment resistant to failure but also precludes the need for a governing central body that every participant trusts. Theoretically, the application of decentralized training in an FL setting could reduce the communication cost when compared with a central server [48], though there is more research conducted on this and there are further avenues that it can take too. Figure 7: An overview of star-network topology.