Federated learning (FL) is an attractive scheme for training machine learning models across distributed edge devices without requiring raw data to be shared. However, deploying FL at the edge of the net- work raises two interrelated issues: (1) the significant communication overhead due to transmitting massive gradient vectors from resource- constrained devices and (2) the risk of privacy leakage via attacks that can invert gradients or infer training samples. Current methods treat gradient compression and differential privacy (DP) as separate concerns, leading to compounded accuracy loss. We propose AGC-DP—Adaptive Gradient Compression (AGC) with Differential Privacy (DP)—a uni- fied framework that achieves efficient communication and strong pri- vacy guarantees across diverse edge FL environments. AGC-DP also introduces a resource-aware client selection algorithm to balance con- tributions from battery level, available memory, and uplink bandwidth; an adaptive compression ratio for each client based on the optimization problem; and a privacy amplification construction that exploits sub- sampling to improve DP accounting. We provide rigorous theoretical analysis, including convergence guarantees under non-i.i.d. data dis- tributions and formal (ϵ, δ)- DP proofs. Comprehensive experiments across the CIFAR-10, HAR, and FEMNIST datasets with up to 100 edge clients show that AGC-DP reduces communication overhead by up to 91.1% compared to vanilla FedAvg, while maintaining accuracy within 1.5% of the vanilla baseline. Our results show that privacy, com- munication efficiency, and model utility can be achieved simultaneously in resource-constrained edge FL.

The computational requirements for swiftly and precisely comprehending lengthy legal documents pose a considerable barrier. The advancement of effective automated summarizing methods is essential for resolving these challenges. Extractive summarization, a widely utilized technique, emphasizes the selection of prominent sentences to abridge extensive publications. The intrinsic subjectivity of this work and the difficulties of obtaining contextual information inside extensive legal documents render it problematic, even for human specialists. To tackle these issues, we suggest an innovative method based on hierarchical transformers. Our concept is based on the stacked transformer encoder design of BERT. In light of the quadratic escalation in computing expense linked to the self-attention mechanism of transformer models as sequence length increases, which constrains their use with lengthy documents, we integrate the Longformer. The Longformer's attention mechanism, which scales linearly with sequence length, enables the analysis of texts with thousands of tokens or more. This mechanism substitutes conventional self-attention with a hybrid method that integrates task-specific global attention and local windowed attention. The suggested model was assessed using two recognized benchmark datasets for long-sequence transformers: BillSum and FIRE. Our experimental findings indicate that it surpasses leading approaches on both datasets. On the BillSum dataset, it attains Rouge-1, Rouge-2, and Rouge-L scores of 47.11, 33.02, and 42.19, respectively. In the FIRE dataset, the respective scores are 58.43, 44.31, and 41.54. These results highlight the enhanced efficacy of our suggested approach relative to current leading techniques.

The Vehicular Ad Hoc Networks (VANETs) have grown in popularity due to its capacity to provide both safety and non-safety services that rely on communication between vehicles and diverse network entities. However, it is crucial to understand that when using these services, malevolent vehicles may take advantage of the situation by spreading misleading information around the network for their benefit. As a result, vehicles must be authenticated to communicate with other vehicles and network entities. The pairing-free and digital signatures have historically been the most widely used signature methods for this purpose, but they can place a heavy computational burden on vehicles during the authentication process. The signing numerous vehicles at once is difficult because the pairing-free signature techniques are often created for single users. The huge key size of digital signatures can also cause them to operate more slowly. In contrast to traditional digital signatures and pairing-free signatures, we uses the Boneh-Gentry-Lynn-Shacham (BGLS) signature scheme for vehicle authentication. The BGLS signature ture method does not rely on computationally expensive bilinear pairingor conventional signature schemes. The proposed model emphasizes effectiveness while providing crucial characteristics like immutability, pseudonymity, decentralization, and scalability.

A mobile ad hoc network (MANET) is a distinctive example of a decentralized, self-organizing, and self-managing wireless ad hoc network. MANET is utilized in several domains such as rescue operations, real-time information dissemination, interpersonal communication, information exchange, and network partitioning. MANET exhibits dynamic alterations in both its structure and nodes to facilitate rapid data transmission. The mobility of nodes presents numerous opportunities for routing design difficulties among them. A list of routing protocols exists, each with unique characteristics, developed for certain locations,, and compared based on various metrics. This study provides a comprehensive overview and comparison of several MANET routing strategies, along with their limitations in ad hoc routing networks.

Blockchain technology provides an extensive network with inherent security attributes, including cryptography, decentralization, and consensus, which enhance trust in transactions. The Internet of Things (IoT) is increasingly recognized as an emergent use of blockchain in finance and security. The fundamental requirement for every blockchain user is to prioritize data security, integrity, and availability. In 2023, trust is essential for the protection of third parties managing private and public privileges. The identified advantages and disadvantages prompted us to conduct an advanced and thorough examination of the applicability of blockchain technology. This study examines security challenges related to blockchain technology and categorizes the security threats across six layers by comparing and analyzing current security methods. The pa per examines and delineates numerous security dangers and challenges related to the implementation of blockchain technology, promoting the oretical exploration and the development of robust security protocols in present and future distributed work environments.