The performance of data restore is one of the key indicators of user experience for backup storage systems. Compared to the traditional offline restore process, online restore reduces downtime during backup restoration, allowing users to operate on already restored files while other files are still being restored. This approach improves availability during restoration tasks but suffers from a critical limitation: inconsistencies between the access sequence and the restore sequence. In many cases, the file a user needs to access at a given moment may not yet be restored, resulting in significant delays and poor user experience. To this end, we present Histore, which builds on the user’s historical access sequence to schedule the restore sequence, in order to reduce users’ access delayed time. Histore includes three restore approaches: (i) the frequency-based approach, which restores files based on historical file access frequencies and prioritizes ensuring the availability of frequently accessed files; (ii) the graph-based approach, which preferentially restores the frequently accessed files as well as their correlated files based on historical access patterns, and (iii) the trie-based approach, which restores particular files based on both users’ real-time and historical access patterns to deduce and restore the files to be accessed in the near future. We implement a prototype of Histore and evaluate its performance from multiple perspectives. Trace-driven experiments on two datasets show that Histore significantly reduces users’ delay time by 4-700 $\times$ with only 1.0%–14.5% additional performance overhead.

In the realm of edge computing, the analog aggregation based Federated Learning Over the Air (FLOA) emerges as a promising technology, offering heightened communication efficiency and privacy provisions. This approach involves concurrent transmission and aggregation, where edge devices (workers) collectively upload their local updates to a Parameter Server (PS) through shared time-frequency resources. The PS then obtains averaged updates only but not the individual local ones, reducing latency and communication costs. However, this simultaneous process exposes FLOA to vulnerabilities, particularly Byzantine attacks. Addressing this concern, we introduce an innovative framework utilizing Unmanned Aerial Vehicles (UAVs) to assist in a heterogeneous FLOA. This framework mitigates the impact of Byzantine attacks while preserving the advantages of over-the-air computation for efficient federated learning. The UAVs engage in over-the-air computation, collecting gradients from local workers and aggregating them. Subsequently, the UAVs transmit these aggregated gradients to the PS in different time slots. Robust aggregation techniques are applied at the PS to combine updates from UAVs. To enhance the robustness of over-the-air transmissions from workers to UAVs, we propose a power control policy. The resilience of the proposed framework to attacks is demonstrated through the derived expected convergence rate, validated by experiments on real datasets.