Traditional disk failure prediction approaches struggle to scale with data growth, as they treat data as a whole collection to obtain the global data view for preprocessing and training. Existing distributed machine learning and stream mining systems are designed to scale data processing, particularly for training. However, scaling disk failure prediction faces challenges in the scalability of preprocessing, including additional data movements from data collection to training, data inflation during preprocessing, and multiple-to-multiple data allocation. To address these challenges, we present SCALEDFP, a general framework for scaling disk failure prediction via multi-source stream mining based on three techniques: near-data preprocessing, random downsampling, and training data allocation. SCALEDFP scales disk failure prediction with the number of data sources. It achieves significant throughput gains of preprocessing and training with comparable prediction accuracy against a state-of-the-art disk failure prediction approach that collects data in a centralized place.

Federated Learning (FL) has been applied across diverse domains as a powerful technique but faces critical challenges in privacy protection. Secure aggregation and additive homomorphic encryption are two of the most commonly used cryptographic methods to protect model updates. To provide a strong privacy guarantee, both methods satisfy the additivity and require the integer form to encrypt model updates. However, they still suffer from a non-negligible overhead of the computation and communication. To mitigate such overhead, existing approaches of lossy compression (e.g., gradient compression) have been explored but exhibit the inapplicability of FL with additive cryptographic methods. In this paper, we propose FedSSA, a novel compression framework to reduce the overhead of additive cryptographic methods in FL based on two new techniques: (i) QSRHT Sketch, a sketch-based compression method that supports large compression ratios with a bounded error with the integer requirement, and (ii) periodic rehashing, which ensures the unbiasedness of QSRHT Sketch. Our evaluation shows that FEDSSA achieves a high compression ratio ($\times 160$) with a low model accuracy degradation (less than 5%). For additive homomorphic encryption, FEDSSA reduces the average computation time per round by up to 58.15 % compared to state-of-the-art compressor that support additive homomorphic encryption, with a low test accuracy drop (within 2.2 %).