FEDERATIV O‘QITISH ASOSIDA MAXFIY MA’LUMOTLAR BILAN ISHLASHDA SUN’IY INTELLEKT MODELLARINI XAVFSIZ HAMKORLIKDA O‘QITISH METODOLOGIYASI
Keywords:
Kalit so‘zlar: sun’iy intellekt, federativ o‘qitish, ma’lumotlar maxfiyligi, differensial maxfiylik, xavfsiz agregatsiya, model xavfsizligi, mashinaviy o‘qitish.Abstract
Annotatsiya: Sun’iy intellekt tizimlarini yuqori sifatli ma’lumotlar asosida o‘qitish ko‘pincha ma’lumotlarni markazlashtirishni talab qiladi. Biroq tibbiyot, moliya, davlat boshqaruvi va telekommunikatsiya kabi sohalarda ma’lumotlar maxfiyligi, huquqiy cheklovlar va tashkilotlararo ishonch masalalari ularni yagona bazaga yig‘ishni qiyinlashtiradi. Ushbu maqolada xom ma’lumotlarni tashkilotlar tashqarisiga chiqarmasdan birgalikda model o‘qitishga imkon beruvchi federativ o‘qitish (federated learning, FL) uchun xavfsizlik va boshqaruvga yo‘naltirilgan metodologik arxitektura taklif qilinadi. Yondashuv mahalliy o‘qitish, vaznlarni agregatsiya qilish, xavfsiz agregatsiya, differensial maxfiylik, ma’lumotlar taqsimotining notekisligi, model sifati va auditni yagona jarayonda ko‘rib chiqadi. Maqola mavjud ilmiy ishlar sinteziga asoslanadi va yangi empirik tajriba o‘tkazilganini da’vo qilmaydi. Uning asosiy hissasi federativ o‘qitish loyihalarini rejalashtirish uchun bosqichma-bosqich protokol, xavf modeli va baholash mezonlarini tizimlashtirishdir. Taklif etilgan metodologiya maxfiylikni saqlashni mutlaq kafolat deb emas, balki texnik, tashkiliy va statistik nazoratlar bilan boshqarilishi lozim bo‘lgan xavf sifatida talqin qiladi.References
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