## 5 A Brief Introduction to Swarm Learning (SL)  
The _Swarm Learning_[14] methodology is a decentralized federated learning paradigm that advocates peer-to-peer collaborative learning by combining edge computing methodologies and blockchain technology. It offers strong guarantees regarding data privacy and secure communication among the nodes, using Blockchain-empowered techniques [122], and hence, it is an appealing means in decentralized networks. For instance, [PERSON] et al. [123] presented a consensus lightweight blockchain protocol, namely TORR, which lessens the latency that conventional blockchain-empowered FL may incur due to its functionality. TORR provides latency reduction since it \"categorizes\" the users as reliable (if they are active and interact with other nodes) or not which means that the former devices are assigned a larger probability, and hence, they are more likely to be randomly chosen to participate in the collaborative learning. Therefore, \"straggling\" nodes affect the training procedure less. In order to protect the system from a _sybil_ attack (in which a node creates non-existing nodes in order to persuade the network that it is interacting with many nodes, hence being reliable, or when a model is asked to be retrieved from a node via hashing decoding and then the \"malicious\" node returns an incorrect model), each node verifies the rest of the nodes in the blockchain. Whenever a node is delayed in answering, the node is excluded of the process as a suspected malicious node. A typical attack in decentralized networks is the Byzantine fault, where malicious nodes aim to infiltrate the network to manipulate or corrupt exchangeable information. [PERSON] et al. [124] presented a decentralized framework that consists of both trainer and validator nodes to deal with the Byzantine fault at the level of _trust_. A validator node receives the updates from its trainers, amalgamates the gradients, and then computes the validation score. The better the score, the more trustworthy the validator node. Another proposed consensus protocol, emphasizing fault tolerance against Byzantine attacks, was described by [PERSON] et al. [125]. In the proposed algorithm, the nodes are considered to be _trusted_ if their accuracy is the same or higher than a specific accuracy threshold, based on both the number of participant nodes and a specific ratio. In Ref. [126], [PERSON] et al. presented a mechanism called Blade-FL based on the PoW consensus protocol in order to succeed secure decentralized federated learning without the need of a central aggregator. Furthermore, they proved that the loss function bound of the global learned model is characterized as convex and depends on the number of the total training rounds. Finally, they analyzed the effect of the _lazy_ nodes (clients) on the general learning process. In Ref. [127], [PERSON] et al. dealt with the privacy concerns of conventional FL by providing a blockchain-based mechanism with differential privacy guarantees (adding Laplace noise). Either the Proof-of-Stake mechanism was used or a block was received from another client (miner) in order to reach a consensus. Finally, the miners with similar gradient values create clusters and build trust among them while they cooperate to learn their cluster's global model. It is also worth highlighting that both quantization and pruning techniques are used in order to alleviate communication among edge devices. There are also published works regarding blockchain architecture implemented in industry 4.0 [128] and in IoT networks [129; 130; 131]. However, SL is a computationally expensive methodology, making its real-time implementation an unfeasible task. Blockchain-based practices in DFL incur high latency and thus more time to reach consensus, compared to centralized federated learning. In real-time applications, there is not much time for checking for malicious nodes/data in the very secure way blockchain-based techniques entail.