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The advent of Ethereum 2.0 has introduced significant changes, particularly the shift to Proof-of-Stake consensus. This change presents new opportunities and challenges for arbitrage. Amidst these changes, we introduce BriDe Arbitrager, a novel tool designed for Ethereum 2.0 that leverages Bribery-driven attacks to Delay block production and increase arbitrage gains. The main idea is to allow malicious proposers to delay block production by bribing validators/proposers, thereby gaining more time to identify arbitrage opportunities. Through analysing the bribery process, we design an adaptive bribery strategy. Additionally, we propose a Delayed Transaction Ordering Algorithm to leverage the delayed time to amplify arbitrage profits for malicious proposers. To ensure fairness and automate the bribery process, we design and implement a bribery smart contract and a bribery client. As a result, BriDe Arbitrager enables adversaries controlling a limited (< 1/4) fraction of the voting powers to delay block production via bribery and arbitrage more profit. Extensive experimental results based on Ethereum historical transactions demonstrate that BriDe Arbitrager yields an average of 8.66 ETH (16,442.23 USD) daily profits. Furthermore, our approach does not trigger any slashing mechanisms and remains effective even under Proposer Builder Separation and other potential mechanisms will be adopted by Ethereum.

Sharding enhances blockchain scalability by partitioning nodes into multiple groups for concurrent transaction processing. Configuring a large number of \emph{small shards} helps improve the transaction concurrency of a sharding system. However, it increases the fraction of malicious nodes within each shard, easily leading to shard corruption and jeopardizing system security. Some existing works have attempted to improve concurrency by reducing the shard size while maintaining security. However, they often require frequent and time-consuming recovery of corrupted shards, leading to severe system stagnation. Also, they usually require network-wide consensus to guarantee security, which limits scalability. To address these issues, we propose DL-Chain, a blockchain sharding system that can securely provide \emph{high concurrency with stable and scalable performance.} Our core idea is a \underline{D}ual-\underline{L}ayer architecture and consensus, which consists of numerous smaller proposer shards (PSs) for transaction processing and multiple larger finalizer committees (FCs) for transaction finalization. To avoid system stagnation and thus guarantee stable performance, we ensure PSs' liveness even if they are corrupted through the cooperation of PSs and FCs, thus eliminating the recovery process of corrupted PSs. To better trade-off security and scalability, we fine-tune the FCs to enable multiple FCs to coexist securely. As a result, DL-Chain allows a larger fraction of malicious nodes in each PS ($<1/2$) and thus can securely configure smaller shards for boosted stable and scalable concurrency. Evaluation results show that DL-Chain achieves up to 10 times improvement in throughput compared to existing solutions and provides stable concurrency with up to 2,550 nodes.

The performance of Federated Learning (FL) hinges on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. This process can cause client drift, especially with significant cross-client data heterogeneity, impacting model performance and convergence of the FL algorithm. To address these challenges, we introduce FedAF, a novel aggregation-free FL algorithm. In this framework, clients collaboratively learn condensed data by leveraging peer knowledge, the server subsequently trains the global model using the condensed data and soft labels received from the clients. FedAF inherently avoids the issue of client drift, enhances the quality of condensed data amid notable data heterogeneity, and improves the global model performance. Extensive numerical studies on several popular benchmark datasets show FedAF surpasses various state-of-the-art FL algorithms in handling label-skew and feature-skew data heterogeneity, leading to superior global model accuracy and faster convergence., Comment: Accepted to CVPR 2024

Federated Deep Learning frameworks can be used strategically to monitor Land Use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for Land Use classification. The need for a Federated approach in this application domain would be to avoid transfer of data from distributed locations and save network bandwidth to reduce communication cost. We use a Federated UNet model for Semantic Segmentation of satellite and street view images. The novelty of the proposed architecture is the integration of Knowledge Distillation to reduce communication cost and response time. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street View and satellite images respectively. Our proposed framework has the potential to be a game-changer in real-time tracking of climate change across the planet.