Table 1.

Summary of the related research.

Approach	Ref	Key Ideas
Distributed data communication constraints	[16]	Wireless communication in edge learning
	[17]	Deep neural network for fog-cloud based with adopting dynamic changes in resource variation
	[18]	Genetic algorithm for scheduling to minimize overall latency
ML at the Edge, Fog, and Cloud levels	[19]	Distributed ML and challenges for implementing (Hardware, security, privacy, and communication)
	[20]	A fruitful survey on distributed machine learning
	[21]	Proposed distributed gradient descent algorithm which fits for non-iid data
Federated ML concepts and applications	[9]	Stochastic method with variance reduction for solving the problem on federated learning
	[10]	Challenges of non-iid Data to Model Training on horizontal and vertical FL
	[11]	Overview of FL, technologies, protocols and applications
	[12]	Horizontal federated learning, vertical federated learning, and federated transfer learning
	[22]	Analyzing Fl regarding data partitioning, privacy, model, and communication
Federated optimization algorithms	[25]	FedAvg, FedProx, CO-OP, FSVRG
	[26]	FSVRG on fog or cloud
	[27]	FedProx
Distribution strategies and hierarchical FL	[28]	Hierarchical FL based on the number of aggregations compared to number of iterations (epochs)
	[29]	Hierarchical FL to minimize training loss and latency
Distributed ML for collaborative PM scenarios	[30]	Distributed PM algorithm based on FL and blockchain
	[13]	Cross-device FL for collaborative PM
	[31]	Real-time fault detection system for edge computing
	[32]	Edge computing in IoT based manufacturing
	[33]	Federated SVM for horizontal FL and federated random forest for vertical FL
	[34]	Novel FL algorithm for the LSTM model for anomaly detection
	[35]	Combination of CNN and LSTM in distributed anomaly detection applications