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The fherma-ai/matmul-batched-gl GitHub repo implements FHERMA's batched matrix-multiplication kernel (P pairs of square matrices) with the DESILO FHE library's GL scheme. It uses a single call, matrix_multiply, on engine shapes (256,16,16), (256,32,32) or (256,64,64), and installs desilofhe==1.17.0 from PyPI at build time rather than vendoring it. A GPU variant differs by one word and is built from the CUDA wheel.
ZDNet Korea (Oct 3, 2026) reports that DESILO and Luxembourg's LHoFT co-hosted the first 'Beyond Silos' fraud-response forum in Luxembourg on Sept 29, with 85 attendees, marking DESILO's expansion into Europe through DESILO Europe. DESILO demoed its HOLMES proof-of-concept, which combines FHE and privacy-preserving ML; in a synthetic test with 3 banks and 60,000 customers, fraud detection within 30 alerts per bank per day rose from 74.4% to 94.5%. DESILO invited 3 or more retail banks to join a HOLMES pilot with real data.
Also in news.google.com, dailysecu.comA DESILO blog post (Sept 29, 2026) benchmarks the GL scheme in the DESILO FHE library against the top FHERMA CKKS implementation (OpenFHE-based) on 256 pairs of 64×64 ciphertext-ciphertext matrix multiplications on one CPU core. GL was up to about 229x faster: roughly 38 min 5 s vs 12.67 s at minimum level (about 180x), and about 6 h 58 min vs 109.44 s at level 16. A matrix multiply consumes 1 level in GL versus 2 in CKKS.
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