提出动态分片联邦学习，解决银行间欺诈检测的效率与安全难题。
Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection
- 将参与方动态分组为小集群，降低通信开销至O(N×m)
- 用线性完整性标签验证梯度一致性，支持91.2%欺诈召回率
- 适合大规模跨机构金融风控场景，抗节点中断
金融欺诈日益跨越机构边界：洗钱网络将交易分散在多家银行，单个机构无法观察全貌。联邦学习（FL）可在不共享原始数据的前提下实现协作检测，但实际部署受限于三大挑战：首先，同态加密计算成本高，难以支撑大规模实时聚合；其次，基于掩码的协议如Google的SecAgg需O(N²)对密钥交换，参与者增多时效率下降；第三，现有协议对提交的梯度更新缺乏有效验证，易受一致性攻击。本文提出动态分片联邦学习（DSFL），用于跨机构欺诈检测。DSFL引入动态随机分片机制，将参与者划分为固定大小为m的密码学临时集群，将通信复杂度降至O(N×m)。每个集群内，参与者提交线性完整性标签——一种加法同态承诺，使服务器可在不解密前提下验证更新一致性。该机制检测不一致更新，而非恶意梯度。此外，主动邻域恢复协议可处理中途掉线问题，重建丢失的掩码。在包含10个模拟银行节点、共284,807笔交易的ULB信用卡欺诈检测数据集上的实验表明，当N=1000时，DSFL的聚合延迟比基于Paillier的方案低约34倍（基于经验基线外推），在20%节点掉线情况下仍保持99%恢复保真度。全局欺诈召回率达91.2%（±0.8%），高于本地训练模型平均68%的水平。
原文摘要 · Abstract (English)
Financial fraud increasingly exploits institutional boundaries: laundering networks distribute transactions across multiple banks because no single institution can observe the full pattern. Federated Learning (FL) enables collaborative detection without raw data sharing, yet practical deployment in banking environments remains constrained by three pressures. First, homomorphic encryption schemes impose high computational costs that limit real-time aggregation at scale. Second, mask-based protocols such as Google's SecAgg require O(N^2) pairwise key exchanges, which become inefficient as participant count grows. Third, existing protocols provide limited verification that submitted gradient updates are well-formed, leaving aggregation vulnerable to consistency attacks. This paper presents Dynamic Sharded Federated Learning (DSFL), a secure aggregation framework for cross-institution fraud detection. DSFL introduces Dynamic Stochastic Sharding, which partitions participants into small cryptographically ephemeral clusters of fixed size m, reducing communication complexity to O(N*m). Within each cluster, participants submit Linear Integrity Tags, additive-homomorphic commitments that allow the server to verify update consistency without decryption. The mechanism detects inconsistent updates rather than malicious gradients. An Active Neighborhood Recovery protocol handles mid-round dropouts by reconstructing orphaned masks. Experiments on the ULB Credit Card Fraud Detection dataset (284,807 transactions across 10 simulated banking nodes) show that DSFL achieves approximately 34x lower aggregation latency than Paillier-based secure aggregation at N=1000, based on analytical extrapolation from empirical baselines, while maintaining 99% recovery fidelity under a 20% dropout regime. Global fraud recall reached 91.2% (+/-0.8%), above the 68% average of locally trained models.
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