Fully homomorphic encryption (FHE) enables privacy-preserving computation on encrypted data but incurs prohibitive computational overhead. To reduce this overhead, leveled FHE (LFHE) limits the multiplicative depth of supported computations. However, this restriction compromises its generality, making it necessary to integrate LFHE with federated learning (FL) and secure multiparty computation (MPC). Yet these hybrid FL/MPC workflows introduce new challenges, as they require dynamic cryptographic adaptation and flexible scheduling for efficient execution. We propose ALOHA, an FPGA-based LFHE acceleration framework designed to eliminate performance and scheduling bottlenecks in hybrid workflows. ALOHA’s hardware engine efficiently directs ciphertext operations to cryptographic modular arithmetic lanes, enabling parallel computational throughput while eliminating data contention. A reconfigurable Benes network dynamically interconnects these lanes, enabling native support for FHE-specific automorphism and adaptive memory access. To bridge algorithmic flexibility with hardware efficiency, ALOHA further integrates a domain-specific compiler that automates ciphertext packing and generates latency-optimized instruction schedules. In the ablation study, the hardware and the compiler contribute 177.8 × and 145.5 × speedup compared to state-of-the-art solutions, respectively. In end-to-end evaluations, ALOHA accelerates complex privacy-preserving services hybrid with FL and MPC, resulting in a speedup of 2.5 × and 3.3 × compared to state-of-the-art accelerators.
Chen et al. (Sat,) studied this question.