Federasyonlu \u00f6\u011frenme (Federated Learning), gizlili\u011fi koruyarak birden fazla cihaz veya kurulu\u015f aras\u0131nda makine \u00f6\u011frenimi modelleri e\u011fitmek i\u00e7in devrim niteli\u011finde bir yakla\u015f\u0131md\u0131r. Ancak bu da\u011f\u0131t\u0131k ve heterojen ortam, algoritmalar\u0131n performans\u0131n\u0131 ve genellenebilirli\u011fini de\u011ferlendirmeyi zorla\u015ft\u0131rmaktad\u0131r. \u0130\u015fte tam bu noktada LEAF (Learning Equality, Accuracy, and Fairness) devreye girerek, federasyonlu \u00f6\u011frenme ara\u015ft\u0131rmalar\u0131 i\u00e7in standartla\u015ft\u0131r\u0131lm\u0131\u015f, tekrarlanabilir ve kapsaml\u0131 bir k\u0131yaslama platformu sunmaktad\u0131r.<\/p>\n
Federasyonlu \u00f6\u011frenme, verilerin yerel cihazlarda kalmas\u0131n\u0131 sa\u011flayarak gizlilik endi\u015felerini giderirken, g\u00fc\u00e7l\u00fc k\u00fcresel modellerin olu\u015fturulmas\u0131na olanak tan\u0131r. Ancak bu paradigman\u0131n kendine \u00f6zg\u00fc zorluklar\u0131 vard\u0131r. LEAF, bu zorluklar\u0131 ele almak ve ara\u015ft\u0131rmac\u0131lara g\u00fcvenilir bir zemin sunmak amac\u0131yla geli\u015ftirilmi\u015ftir.<\/p>\n
Geleneksel makine \u00f6\u011freniminden farkl\u0131 olarak, federasyonlu \u00f6\u011frenme ortamlar\u0131 genellikle veri heterojenli\u011fi, a\u011f gecikmeleri, cihaz \u00e7e\u015fitlili\u011fi ve gizlilik k\u0131s\u0131tlamalar\u0131 gibi karma\u015f\u0131k sorunlarla kar\u015f\u0131la\u015f\u0131r. Her istemcinin verisi farkl\u0131 da\u011f\u0131l\u0131mlara sahip olabilir, bu da modelin genellenebilirli\u011fini ve adil performans\u0131n\u0131 etkileyebilir.<\/p>\n
Federasyonlu \u00f6\u011frenme algoritmalar\u0131n\u0131n etkinli\u011fini ve verimlili\u011fini do\u011fru bir \u015fekilde de\u011ferlendirmek i\u00e7in standartla\u015ft\u0131r\u0131lm\u0131\u015f veri setlerine ve de\u011ferlendirme protokollerine ihtiya\u00e7 vard\u0131r. Farkl\u0131 ara\u015ft\u0131rmac\u0131lar\u0131n kendi veri setleri ve y\u00f6ntemleriyle yapt\u0131\u011f\u0131 \u00e7al\u0131\u015fmalar\u0131n kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131 zordur. LEAF, bu bo\u015flu\u011fu doldurarak ortak bir zemin sa\u011flar.<\/p>\n
LEAF’in temel amac\u0131, federasyonlu \u00f6\u011frenme algoritmalar\u0131n\u0131n performans\u0131n\u0131, adilli\u011fini ve gizlilik etkilerini \u00e7e\u015fitli ger\u00e7ek d\u00fcnya senaryolar\u0131nda de\u011ferlendirmektir. Bu sayede ara\u015ft\u0131rmac\u0131lar, yeni algoritmalar\u0131n\u0131 objektif bir \u015fekilde test edebilir ve mevcut \u00e7\u00f6z\u00fcmlerle kar\u015f\u0131la\u015ft\u0131rabilirler.<\/p>\n
LEAF, federasyonlu \u00f6\u011frenme ara\u015ft\u0131rmac\u0131lar\u0131n\u0131n ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131lamak \u00fczere tasarlanm\u0131\u015f zengin \u00f6zelliklere ve \u00e7e\u015fitli veri setlerine sahiptir. Bu \u00f6zellikler, platformu hem esnek hem de kapsaml\u0131 k\u0131lar.<\/p>\n
LEAF, metin s\u0131n\u0131fland\u0131rmas\u0131, g\u00f6r\u00fcnt\u00fc tan\u0131ma, duygu analizi gibi \u00e7e\u015fitli makine \u00f6\u011frenimi g\u00f6revlerini destekler. Desteklenen veri setleri aras\u0131nda LEAF’teki veri setleri, ger\u00e7ek d\u00fcnya da\u011f\u0131t\u0131mlar\u0131n\u0131 ve kullan\u0131c\u0131 davran\u0131\u015flar\u0131n\u0131 yans\u0131tacak \u015fekilde tasarlanm\u0131\u015ft\u0131r. \u00d6rne\u011fin, Federasyonlu \u00f6\u011frenmenin en b\u00fcy\u00fck zorluklar\u0131ndan biri veri heterojenli\u011fidir. LEAF, bu durumu do\u011fal olarak i\u00e7eren veri setleri sunarak algoritmalar\u0131n bu t\u00fcr ko\u015fullar alt\u0131nda nas\u0131l ba\u015fa \u00e7\u0131kt\u0131\u011f\u0131n\u0131 de\u011ferlendirme imkan\u0131 tan\u0131r. Her istemcinin veri da\u011f\u0131l\u0131m\u0131 farkl\u0131 olabilir ve bu, modellerin genellenebilirli\u011fini do\u011frudan etkiler.<\/p>\n Tablo 1: LEAF Veri Setlerinden Baz\u0131lar\u0131<\/strong><\/p>\nFEMNIST<\/code> (federasyonlu MNIST), Shakespeare<\/code> (metin tahmini), Sent140<\/code> (duygu analizi) ve CelebA<\/code> (y\u00fcz \u00f6znitelik tan\u0131ma) gibi pop\u00fcler se\u00e7enekler bulunmaktad\u0131r. Bu \u00e7e\u015fitlilik, farkl\u0131 uygulama alanlar\u0131ndaki algoritmalar\u0131n test edilmesine olanak tan\u0131r.<\/p>\nGer\u00e7ek D\u00fcnya Senaryolar\u0131<\/h3>\n
FEMNIST<\/code> veri seti, farkl\u0131 kullan\u0131c\u0131lar\u0131n farkl\u0131 el yaz\u0131s\u0131 stillerine sahip olabilece\u011fi senaryoyu taklit eder. Bu, algoritmalar\u0131n ger\u00e7ek d\u00fcnya ko\u015fullar\u0131nda nas\u0131l performans g\u00f6sterdi\u011fine dair de\u011ferli i\u00e7g\u00f6r\u00fcler sunar.<\/p>\nVeri Heterojenli\u011fi Y\u00f6netimi<\/h3>\n
\n\n
\n \nVeri Seti<\/th>\n G\u00f6rev T\u00fcr\u00fc<\/th>\n \u0130stemci Say\u0131s\u0131 (\u00d6rnek)<\/th>\n Veri Heterojenli\u011fi<\/th>\n<\/tr>\n<\/thead>\n \n FEMNIST<\/td>\n G\u00f6r\u00fcnt\u00fc S\u0131n\u0131fland\u0131rma<\/td>\n 3500+<\/td>\n Y\u00fcksek (Kullan\u0131c\u0131ya G\u00f6re)<\/td>\n<\/tr>\n \n Shakespeare<\/td>\n Metin Tahmini<\/td>\n 100+<\/td>\n","protected":false},"excerpt":{"rendered":"Federasyonlu \u00f6\u011frenme (Federated Learning), gizlili\u011fi koruyarak birden fazla cihaz veya kurulu\u015f aras\u0131nda makine \u00f6\u011frenimi modelleri e\u011fitmek i\u00e7in devri…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-37030","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n