Related papers: AI-driven Predictive Shard Allocation for Scalable…
In this paper, we propose two models for scaling the transaction throughput in Proof-of-Work (PoW) based blockchain networks. In the first approach, a mathematical model has derived for optimal transaction throughput for PoW based longest…
Nowadays, sharding is deemed as a promising way to save traditional blockchain protocols from their low scalability. However, such technique also brings several potential risks and huge communication overheads. An improper design may give…
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems.…
Sharding is essential for improving blockchain scalability. Existing protocols overlook diverse adversarial attacks, limiting transaction throughput. This paper presents Reticulum, a groundbreaking sharding protocol addressing this issue,…
With the development of decentralized consensus protocols, permissionless blockchains have been envisioned as a promising enabler for the general-purpose transaction-driven, autonomous systems. However, most of the prevalent blockchain…
Embedding learning is an important technique in deep recommendation models to map categorical features to dense vectors. However, the embedding tables often demand an extremely large number of parameters, which become the storage and…
In today's blockchain system, designing a secure and high throughput blockchain on par with a centralized payment system is a difficult task. Sharding is one of the most worthwhile emerging technologies for improving the system throughput…
Blockchain's decentralization, transparency, and tamper-resistance properties have facilitated the system's use in various application fields. However, the low throughput and high confirmation latency hinder the widespread adoption of…
This thesis proposes techniques aiming to make blockchain technologies and smart contract platforms practical by improving their scalability, latency, and privacy. This thesis starts by presenting the design and implementation of…
Recently, permissioned blockchain has been extensively explored in various fields, such as asset management, supply chain, healthcare, and many others. Many scholars are dedicated to improving its verifiability, scalability, and performance…
Sharding a large machine learning model across multiple devices to balance the costs is important in distributed training. This is challenging because partitioning is NP-hard, and estimating the costs accurately and efficiently is…
The development of blockchain applications increased the demand for blockchain performance. Among the attempts of many blockchain scale-out solutions, sharding can improve performance and reduce the storage requirements of each node.…
Jointly achieving safety and efficiency in human-robot interaction (HRI) settings is a challenging problem, as the robot's planning objectives may be at odds with the human's own intent and expectations. Recent approaches ensure safe robot…
Decentralized resource markets are Web 3.0 applications that build open-access platforms for trading digital resources among users without any central management. They promise cost reduction, transparency, and flexible service provision.…
Traditional Blockchain Sharding approaches can only tolerate up to n/3 of nodes being adversary because they rely on the hypergeometric distribution to make a failure (an adversary does not have n/3 of nodes globally but can manipulate the…
Scalable ordered maps must ensure that range queries, which operate over many consecutive keys, provide intuitive semantics (e.g., linearizability) without degrading the performance of concurrent insertions and removals. These goals are…
Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has attracted widespread attention as a new decentralized learning pattern. However, traditional consensus…
Recent account allocation studies in sharded blockchains are typically miner-driven, requiring miners to perform global optimizations for all accounts to enhance system-wide performance. This forces each miner to maintain a complete copy of…
Most machine learning and deep neural network algorithms rely on certain iterative algorithms to optimise their utility/cost functions, e.g. Stochastic Gradient Descent. In distributed learning, the networked nodes have to work…
Achieving high availability and robust security in Kubernetes requires more than reactive scaling and standard perimeter firewalls. Traditional autoscalers, such as HPA, often fail to react quickly to traffic spikes and cannot distinguish…