The federated learning (FL) principle ensures multiple clients jointly develop a machine learning model without exchanging their local data. Various government enacted prohibition policies on data exchange between organizations have led to the need for privacy-preserved federated learning. Many industries have cultivated this idea of model development through federated learning to enhance performance and accuracy. This paper offers a detailed overview of the background of FL, highlighting existing aggregation algorithms, frameworks, implementation aspects, and dataset repositories, establishing itself as an essential reference for researchers in the field. The paper thoroughly reviews existing centralized and decentralized FL approaches proposed in the literature and gives an overview about the methodology, privacy techniques implemented and limitations to guide other researchers to advance their research in the field of federated learning. The paper discusses the critical role of privacy-enhancing technologies like differential privacy (DP), homomorphic encryption (HE), and secure multiparty computation (SMPC) in federated learning highlighting their effectiveness in safeguarding sensitive data while optimizing the balance between privacy, communication efficiency, and computational cost. The paper explores the applications of federated learning in privacy-sensitive areas like natural language processing (NLP), healthcare, and Internet of Things (IoT) with edge computing. We believe our work provides a novel addition by identifying privacy evaluation metrics and spotlighting the measures in terms of data privacy and correctness, communication cost, computational cost and scalability. Furthermore, it identifies emerging challenges and suggests promising research directions in the federated learning domain.

The high demand for user-centric applications such as secure cloud storage laid the foundation for the development of user-centric security schemes with multiple security features in recent years. But, the current state-of-art techniques primarily emphasized only one type of security feature i.e., either homomorphism or non-malleability. To fill this gap and provide a common platform for both homomorphic and non-malleable cloud applications, we have introduced a new public key-based probabilistic encryption switching method. The proposed scheme generates $${\mathcal {O}}$$O (m+2 log N) bits of ciphertext where $$m\in {\mathbb {N}}$$m ∈ N and $$m<n$$m < n , $$n\in {\mathbb {N}}$$n ∈ N is the plaintext size, N is the RSA composite. The superior performance of the proposed scheme has been tested in comparison to existing methods and is reported in this paper.

The fast proliferation of internet-enabled devices generates massive amounts of data every day from every aspect of life. These internet-enabled devices lack the storage, processing power, and capacity necessary to handle and store this massive amount of accurate and volumetric data. Cloud computing has been proposed as a compelling substitute to process these requests. However, the ingress traffic to the Cloud is huge which causes latency overhead due to the gap that exists between end devices and the Cloud datacentre. Additionally, processing these dynamic and heterogeneous requests with disparate requirements is a computationally NP-hard problem. In this regard, Fog computing appears to be an appealing solution to surpass the aforementioned challenges as a complementary to Cloud. Therefore, this research integrated a Fog layer between the end devices and Cloud datacenters enabling a collaborative computation framework. For the classification of requests and determining the target layers for processing, this research suggests an Adaptive Neuro-Fuzzy Inference System (ANFIS). Furthermore, an improved Honey Badger Algorithm (IHBA) is implemented for scheduling those requests at the target layer. To improve the convergence rate of the traditional HBA, a chaos mapping function has been implemented with an Opposition-based Learning (OBL) method. The proposed task consolidation approach has been validated using extensive simulations for QoS parameters on real-world benchmark datasets. With percentage improvements of 13.69%, 28.4%, 33.85%, 7.66%, 26.48%, and 5.63% for makespan, resource utilization, energy consumption, service delay, service cost, and delay violation, respectively, the obtained simulation results show that the suggested algorithm works better than the current state-of-the-art.

The vast production of resource-constrained wireless communication devices and the development of various techniques in recent years opens a room for security concerns to overcome potential attacks. However, efficient methods are needed to reduce the trade-off between communication and computation complexities in resource-constrained wireless device communication. In this paper, we propose a lightweight fault-tolerant secure data communication framework that consists of Elliptic Curve Diffie-Hellman (ECDH) secure communication scheme, Elliptic Curve Cryptography (ECC) based secure communication scheme and Elliptic Curve Integrated Encryption Scheme (ECIES) based authentication scheme for wireless sensor network communication using Message Passing Interface (MPI) parallel program platform. Further, we have implemented the proposed framework for single sink node (scenario-1) and all sink node (scenario-2) scenarios with parallel threads using Linux Pthreads to improve the total execution time. It is observed that the overall execution time performance of ECC is better in scenario-2 whereas performance of ECDH is better in scenario-1 when number of sensors greater than 200. It is also observed that enabling Linux Pthreads in ECC implementation guarantees the parallel execution of decryption process and reduction in the overall execution time in both the scenarios. The results demonstrate the superiority of the proposed framework in terms of execution time and memory use over simulated wireless network environments, making the proposed framework suitable for fault-tolerant wireless sensor communication applications.

In this article, we proposed two schemes for generating a broadband optical frequency comb (OFC) source using dual laser sources. In the first proposed technique, the OFC is generated using a cascaded single-drive Mach-Zehnder modulator (MZM) and dual-drive Mach-Zehnder modulator (DD-MZM). Here, the two CW light sources are modulated by each of the MZM, respectively. The first single-drive MZM generates the subcarriers, while The second DD-MZM carries out intensity modulation on the output spectrum generated by the MZM, ensuring that the power fluctuations of both even and odd sidebands are adjusted to an equal level. The second approach involves replacing the single-drive MZM with a phase modulator (PM). The center frequencies of laser 1 and laser 2 must be carefully set to achieve a non-overlapping or gap-free comb spectrum. The frequency of the RF signal driving the modulators determines the spacing between the laser frequencies. In order to illustrate this aspect, we have utilized three distinct RF signal frequencies: 10 GHz, 16 GHz, and 20 GHz. Furthermore, we have examined the constraints that impact the estimation of the center frequency of laser 2, aiming for a non-overlapping and gap-free comb spectrum. By considering these constraints, we can ensure the successful generation of a uniform and stable comb spectrum with the desired characteristics in our proposed approach. The theoretical and primary analysis of cascaded single-drive MZM and DD-MZM, as well as cascaded PM and DD-MZM, is also investigated, and simulation results confirm the findings. By employing our proposed approach, OFC achieves a significant number of comb lines while exhibiting a power deviation of ~ 2 dB in comparison to single laser configurations.

IoT and Smart devices are typically deployed for real-time applications that need to communicate to the cloud infrastructure for data processing and storage. However, the cloud infrastructure has high network latency, and hence the fog has been introduced to form a layered cloud-fog-device framework. The layered architecture requires secure and efficient authentication between all the communicating entities. Secure authentication between fog nodes and cloud servers is not addressed in previous authentication schemes, which can result in severe threats like server masquerading and insider attacks. Ali et al. proposed an authentication key exchange scheme for the cloud-fog-device framework, which was found to be vulnerable to key revelation attacks and failed to provide user anonymity and session secrecy. To overcome the security issues identified, an improved authentication scheme based on key agreement and management was proposed. The scheme authenticates all the entities in the communication, including the cloud server. The scheme secures against privileged insider attacks, ensures user anonymity, untraceabilty, and session secrecy. The scheme was verified using rigorous cryptanalysis and its security was proved using the ROR model. Formal verification using scyther also confirmed its security against active and passive attacks. An efficiency analysis was performed by comparing the computation and communication costs with other relevant schemes. Functional analysis proved that the proposed scheme exhibits all the functionalities necessary for robust authentication in the cloud-fog-device framework. Overall, the new authentication scheme addresses the security concerns of the cloud-fog-device framework, making it a secure and reliable option for real-time applications.