Table 1.

Overview of Federated Learning Research covered with key ideas, methodologies, open issues and opportunities for future research.

Area	Ref.	Motivation and Key Idea	Proposed Approach	Open Issues and Further Opportunities
Protocol	[138]	Scalable production system for Federated Learning	Standard protocol as a basis for Federated Learning	Need for optimization for application-specific scenarios
	[139]	Promote client selection under heterogeneous resource scenarios	FedCS protocol to select users based on their resource availability	Relies on the truthfulness of user resource availability submissions.
	[140]	Federated Learning for traffic prediction models	Suitable protocol for small-scale Federated Learning enabled traffic control	Extension to larger scale of recruited clients.
Aggregation	[141]	A standard aggregation method	FedAvg algorithm to aggregate the average model parameters of updates	An alternative to equally weighing all local model updates during aggregation.
	[143]	Optimize Federated Learning in heterogeneous networks	Proximal parameter to limit the impact of variable updates allowing partial work to be done	Solutions for the cases where not all updates are of positive contribution
	[144]	Optimize Federated Learning through data distribution	Loss-based Adaptive Boosting to compare local model losses prior to aggregation	Extensions to consider heterogeneous contribution scenarios during aggregation
Reputation Models	[145]	Incentive to promote reliable Federated Learning	Multi-weight subjective logic to formulate reputation scores	Advanced reputation scores to directly reflect performance of users
	[146]	Enhanced client selection to improve model performance	Local model performance metrics to formulate reputation scores	Minimum computational overhead for assessment of reputation scores for every user
	[147]	Reputation-awareness	Interaction records to generate reputation opinions	Reputation scores to reflect performance of users directly
Differential Privacy	[150]	Enhanced privacy preservation through sketching	Obfuscation of the original data to achieve differential privacy	Performance versus privacy gain
	[41]	Differential privacy in Federated Learning	Noise before model aggregation	Considering varied size and distribution of user data
	[151]	Enhanced privacy and efficiency of Federated Learning in industrial AI applications	Add noise according to Gaussian distribution to local models	Extensive analyses on high-dimensional data
BlockChain	[152]	Accountable Federated Learning	Combine aggregator and blockchain to preserve privacy of users	Fairness assurance in participant rewarding
	[153]	Enhanced privacy for Federated Learning	Noise at the initial stage onto the original data, and use BlockChain to facilitate the Federated Learning process	Tackle potential performance issues due to noising too early
	[154]	Improved fairness and privacy in Federated Learning	Scale rewards with respect to participant contribution	Extension to non-IID scenarios
	[155]	Privacy Preserving Federated Learning for industrial IoT applications	Use blockchain with Inter-Planetary File System (IPFS) and noise to local model features	Extension to non-IID data or heterogeneous device scenarios