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Bitcoin, as well as many of its successors, require the whole transaction record to be reliably acquired by all nodes to prevent double-spending. Recently, many blockchains have been proposed to achieve scale-out throughput by letting nodes…
Distributed LLM inference requires careful coordination of parallelization strategies across hundreds to thousands of NPUs to meet production SLOs. Current systems like Megatron-LM rely on static heuristics that separately configure…
Blockchain technology, while revolutionary in enabling decentralized transactions, faces scalability challenges as the ledger must be replicated across all nodes of the chain, limiting throughput and efficiency. Sharding, which divides the…
Although blockchain, the supporting technology of Bitcoin and various cryptocurrencies, has offered a potentially effective framework for numerous applications, it still suffers from the adverse affects of the impossibility triangle.…
Consensus protocols are currently the bottlenecks that prevent blockchain systems from scaling. However, we argue that transaction execution is also important to the performance and security of blockchains. In other words, there are ample…
In this work, we propose a stateless blockchain called CompactChain, which compacts the entire state of the UTXO (Unspent Transaction Output) based blockchain systems into two RSA accumulators. The first accumulator is called Transaction…
Increasing the transactional throughput of decentralized blockchains in a secure manner has been the holy grail of blockchain research for most of the past decade. This paper introduces a scheme for scaling blockchains while retaining…
Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it becomes more difficult to implement and manage a blockchain…
In this paper, we propose a scheme called securePrune for reducing the storage space of a full node and synchronization time of bootstrapping nodes joining the Peer-to-Peer (P2P) network in an Unspent Transaction Outputs (UTXO) based…
The recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's…
Blockchain offers a decentralized, immutable, transparent system of records. It offers a peer-to-peer network of nodes with no centralised governing entity making it unhackable and therefore, more secure than the traditional paper-based or…
Recent advancements in machine learning (ML), natural language processing (NLP), and foundational models have shown promise for real-life applications in critical, albeit compute-constrainted fields like healthcare. In such areas, combining…
Most blockchain platforms from Ethereum onwards render smart contracts as stateful reactive objects that update their state and transfer crypto-assets in response to transactions. A drawback of this design is that when users submit a…
We provide a UTXO model of blockchain transactions that is able to represent both credit and debt on the same blockchain. Ordinarily, the UTXO model is solely used to represent credit and the representation of credit and debit together is…
Blockchain technology, with implications in the financial domain, offers data in the form of large-scale transaction networks. Analyzing transaction networks facilitates fraud detection, market analysis, and supports government regulation.…
Scalability is one of the main roadblocks to business adoption of blockchain systems. Despite recent intensive research on using sharding techniques to enhance the scalability of blockchain systems, existing solutions do not efficiently…
Sharding distributed ledgers is a promising on-chain solution for scaling blockchains but lacks formal grounds, nurturing skepticism on whether such complex systems can scale blockchains securely. We fill this gap by introducing the first…
The use of ML in cybersecurity has long been impaired by generalization issues: Models that work well in controlled scenarios fail to maintain performance in production. The root cause often lies in ML algorithms learning superficial…
Machine learning algorithms learn from data and use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the machine learning process is often difficult to automate. A…
Machine learning (ML) has been pervasively researched nowadays and it has been applied in many aspects of real life. Nevertheless, issues of model and data still accompany the development of ML. For instance, training of traditional ML…