ABSTRACT:This literature review synthesizes existing research on the intersection of federated deep learning, explainable AI (XAI), and privacy preservation for breast cancer detection. It highlights the critical need for robust and transparent AI models in medical diagnostics while addressing the inherent challenges of data privacy in healthcare. The review identifies current trends in model architectures, privacy-enhancing techniques, and XAI methodologies applied to this domain. By critically analyzing the strengths and weaknesses of various approaches, this paper aims to establish a foundational understanding for future research, pinpointing gaps and contradictions in the current literature. This work does not present new experimental results but rather provides a comprehensive overview and critical assessment of the state-of-the-art.

KEYWORDS: Federated Learning, Deep Learning, Explainable AI, Breast Cancer Detection, Privacy Preservation, Medical Imaging etc.

AN EXPLAINABLE FEDERATED DEEP LEARNING FRAMEWORK FOR BREAST CANCER DETECTION WITH PRIVACY PRESERVATION

MS. PUNAM KHOBRAGADE , PROF. NILESH NAGRALE ,PROF. SUSHIL BHISE

DEPARTMENT OF INFORMATION TECHNOLOGY, TULSIRAMJI GAIKWAD-PATIL COLLEGE OF ENGINEERING & TECHNOLOGY, NAGPUR, INDIA