Shengyun Liu

Feng Zhu, Jingjing Zhang, Shengyun Liu et al.

Local stochastic gradient descent (SGD) is a fundamental approach in achieving communication efficiency in Federated Learning (FL) by allowing individual workers to perform local updates. However, the presence of heterogeneous data distributions across working nodes causes each worker to update its local model towards a local optimum, leading to the phenomenon known as ``client-drift" and resulting in slowed convergence. To address this issue, previous works have explored methods that either introduce communication overhead or suffer from unsteady performance. In this work, we introduce a novel metric called ``degree of divergence," quantifying the angle between the local gradient and the global reference direction. Leveraging this metric, we propose the divergence-based adaptive aggregation (DRAG) algorithm, which dynamically ``drags" the received local updates toward the reference direction in each round without requiring extra communication overhead. Furthermore, we establish a rigorous convergence analysis for DRAG, proving its ability to achieve a sublinear convergence rate. Compelling experimental results are presented to illustrate DRAG's superior performance compared to state-of-the-art algorithms in effectively managing the client-drift phenomenon. Additionally, DRAG exhibits remarkable resilience against certain Byzantine attacks. By securely sharing a small sample of the client's data with the FL server, DRAG effectively counters these attacks, as demonstrated through comprehensive experiments.

Rongji Huang, Yifeng Ye, Gerui Wang et al.

Due to regulatory compliance and governance management, modern (permissioned) blockchains require flexible endorsement, which allows the endorsement policy for each contract or state object to be individually defined. To enable flexible endorsement, Hyperledger Fabric employs an execute-order-validate (EOV) paradigm, in which transactions first undergo speculative execution and endorsement, and are only then ordered and validated. Meanwhile, most blockchain systems, including the platform targeted in this work (i.e., ChainMaker), still follow a conflict-free order-execute framework. We argue that the EOV paradigm still faces several limitations, notably high abort rates in high-contention workloads such as those in Decentralized Finance (DeFi). To avoid refactoring our system and better suit DeFi applications, we try to integrate flexible endorsement into the classical order-execute architecture and accordingly propose a new framework. The key challenge is to deterministically remove problematic transactions from an ordered list, while preserving censorship resistance and decentralization for the remaining ones. We instantiate this framework on top of Tendermint, a seminal Byzantine fault-tolerant (BFT) protocol adopted in our system, and thereby propose FlexTender. By elegantly embedding endorsements into consensus, FlexTender incurs no additional messaging overhead in the normal case. Empirical evaluation using an Ethereum USDT workload demonstrates that FlexTender achieves up to $10.6\times$ speedup in throughput over an EOV simulation on the same platform.