Dr. Siladittya Manna
I investigate vulnerabilities in PEFT-based federated learning frameworks and develop trustworthy distributed AI systems.
Academic Background
About Me
Foundation (Ph.D. at ISI Kolkata): Self-supervised representation learning and label-efficient medical image analysis ➔ Distributed Expansion (HKBU & IISc): Federated learning across decentralized medical data with non-IID heterogeneity ➔ Current Frontier (Postdoc at IISc): AI security, gradient inversion attacks, and privacy vulnerabilities in parameter-efficient fine-tuning (PEFT) adapters.
Who I Am
I am an ANRF Postdoctoral Fellow at the Indian Institute of Science (IISc), Bangalore, working in the Department of Computational and Data Sciences with Prof. Anirban Chakraborty. Prior to IISc, I was a Senior Research Assistant at Hong Kong Baptist University with Prof. Yiu-Ming Cheung, and completed my Ph.D. in Computer Science at the Indian Statistical Institute (ISI), Kolkata, under the supervision of Prof. Umapada Pal.
Research Vision
My overarching goal is to build privacy-preserving, adversarial-resilient distributed AI systems. When healthcare providers or autonomous agents collaborate across institutions, sharing parameters must not compromise proprietary data. I investigate how parameter-efficient adapters (PEFT) and foundation models can be secured against inversion attacks, poisoning, and information leakage without sacrificing convergence speed.
“Building trustworthy machine learning systems that learn collaboratively while preserving privacy, security, and sample efficiency.”
Prospective Students & Research Collaborations
I actively mentor undergraduate and graduate students in federated learning, gradient inversion attacks, and self-supervised medical computer vision. If you have strong programming skills in PyTorch, solid mathematical grounding, and a keen interest in trustworthy AI, feel free to reach out with your CV and research statement.
Recent Updates
Latest News
ANRF National Post-Doctoral Fellowship (2026–2028)
Awarded the prestigious ANRF National Post-Doctoral Fellowship by the Anusandhan National Research Foundation (DST, Govt. of India). Hosted at the Department of CDS, IISc Bangalore, under the mentorship of Prof. Anirban Chakraborty.
Reliability-Aware Spatio-Temporal Maps for Heart Rate Monitoring (ICIP 2026)
Co-authored with Arpan Bairagi, Rakesh Dey, and Prof. Umapada Pal (ISI Kolkata). Accepted and to be presented at IEEE International Conference on Image Processing (ICIP 2026).
Semi-Supervised Sperm Motility Classification on VISEM-Tracking (AI4Fertility 2026)
Co-authored with Suyash Kumar, Ankur Singh (IIT BHU), and Dr. Rajkumar Saini (LUT University, Sweden). To be presented at The First Workshop on AI in Fertility Science 2026.
Residual Dense Blocks for Foreground Imbalance in Brachytherapy Applicator Segmentation
Published in Springer Nature Computer Science. Co-authored with Suresh Das, Prasun Sanki, Subhayan Mondal, Dr. Saumik Bhattacharya, and Dr. Sayantari Ghosh.
Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification
Published in ACM Transactions on Asian and Low-Resource Language Information Processing (TALIP) with Shree Mitra and Arkadip Maitra.
Scholarly Output
Selected Publications
Peer-reviewed journal articles, conference proceedings, survey papers, and doctoral research in Self-Supervised Learning and Medical AI.
Ongoing Projects
Active research investigations, manuscripts under review, and ongoing collaborative studies across Federated Learning security and medical AI.
Team
Colleagues, researchers, and students I am currently collaborating with at the Indian Institute of Science (IISc), Bangalore.
Core Pillars
Research Areas
Integrated research domains bridging distributed optimization, threat analysis, representation learning, and medical AI.
Federated Learning
Developing resilient distributed learning algorithms that train collaboratively across decentralized, non-IID client partitions with parameter-efficient fine-tuning (PEFT) and adaptive aggregation.
AI Security & Privacy
Auditing and fortifying learning systems against adversarial threats, including gradient inversion attacks, prompt poisoning in foundation models, and verifiable machine unlearning.
Self-Supervised Representation Learning
Advancing contrastive and generative pretext objectives that acquire transferable, sample-efficient visual representations from unlabeled and multimodal data.
Medical AI & Clinical Diagnostics
Bridging machine learning theory with clinical healthcare, focusing on label-efficient organ segmentation, rare foreground imbalance, and safe diagnostic assistance.
Current Research Questions
- 1.How do parameter-efficient fine-tuning (PEFT) adapters interact with gradient inversion vulnerabilities during federated model synchronization?
- 2.Can decentralized vision-language models maintain robustness against prompt poisoning and adversarial manipulation across heterogeneous clients?
- 3.How can self-supervised representation learning guarantee label-efficiency in clinical imaging without succumbing to severe domain shift?
- 4.How can we reconcile client personalization with formal differential privacy and Byzantine fault tolerance in distributed optimization?
Long-Term Research Goals
- Trustworthy Distributed AI: Establish distributed learning frameworks whose empirical performance matches centralized baselines while offering verifiable security against gradient leakage.
- Sample-Efficient Representation Learning: Devise self-supervised pretext tasks that transfer seamlessly across multimodal domains with minimal manual curation.
- Secure Cross-Institutional Collaboration: Enable hospitals and research institutions to train state-of-the-art shared models without transmitting raw patient data.
- High-Stakes Clinical Translation: Deploy robust, interpretable computer vision algorithms for real-world clinical interventions in oncology and reproductive health.
Honors & Grants
Awards & Funding
ANRF National Post-Doctoral Fellowship (N-PDF)
Anusandhan National Research Foundation (ANRF), Department of Science & Technology, Government of India
The ANRF National Post-Doctoral Fellowship is one of the highest individual postdoctoral honors in India, awarded through rigorous national peer-review to support independent, early-career researchers conducting high-impact scientific inquiry.
Academic Milestones
Journey & Experience
Post-Doctoral Fellow (ANRF Fellow)
Investigating privacy vulnerabilities in parameter-efficient fine-tuning (PEFT) adapters and distributed multimodal architectures.
Post-Doctoral Research Associate
Research on vulnerabilities in Transformer-based federated learning and distributed optimization frameworks.
Senior Research Assistant
Conducted research on federated learning algorithms applied to medical image segmentation, augmented by self-supervised learning principles.
Ph.D. in Computer Science
Thesis: Self-Supervised Learning and its Applications in Medical Image Analysis. Awarded doctoral degree with distinction.
M.Tech in VLSI & Microelectronics
Dual Degree program with specialization in VLSI architectures and microelectronics.
B.Tech in Electronics & Telecommunication
Undergraduate curriculum covering signals, statistical systems, and communication engineering.
Scholarly Reach
Impact & Global Network
Automated citation metrics synced with Google Scholar, coupled with an active collaborative network across France, Germany, Sweden, Hong Kong, and India.
Citations per Year
International & National Collaborations
Partners in France, Germany, Sweden, Hong Kong, and leading Indian institutes
University of Lille 1 S&T
Collaboration Focus: Document Analysis & Pattern Recognition
Academic Service & Leadership
Contributing to the scientific community through various roles and initiatives
1. Editorial Activities
Journal Reviewer
Conference Reviewer
2. Teaching & Mentorship
Teaching Assistant
Mentorship
3. Tutorials & Invited Talks
Curated Research Reading Lists
Carefully curated, active reading lists and literature repositories tracking the latest advances across key subfields. These static bibliographies serve as comprehensive entry points for researchers and students.
Decentralized optimization, personalized client modeling, and non-IID data robustness.
Methods for reconstructing private client training inputs from model gradients and activations.
Adversarial prompt injection vulnerabilities in vision-language models and LLMs.
Algorithmic data erasure and forgetting guarantees in trained deep models.
Selective revocation of client contributions without retraining global federated models.
Concepts and benchmarks for targeted knowledge ablation in foundation models.
Backdoor injections and malicious trigger mechanisms in prompt-tuned architectures.
Byzantine fault tolerance, model poisoning, and convergence guarantees under attack.
AI Projects
Research Paper Summarizer
Multi-modal document analysis leveraging VLM-based OCR and Gemma models for structured academic summarization.
Scholarly Articles Discovery Interface
System that aggregates newly published literature, filters topic relevance, and synthesizes annotated bibliographies.
Medical Image Viewer & Foundation Backend
In-browser viewer supporting DICOM and NIfTI formats, integrated with foundation model backends for exploratory segmentation.
Blog: The Owl
Updating your Local Branch from a Specific Remote Branch in Git
Best practices for keeping local Git branches synchronized with upstream features cleanly.
Running MedGemma-4B on a Small GPU (<16GB) Using BitsAndBytes
Quantization strategies and memory budgeting for serving 4B parameter multimodal models locally.
The Practical Guide to Distributed Training using PyTorch: Multi-Node with SLURM
Configuring multi-node DDP workloads and master communication across academic cluster schedulers.
Get in Touch
Contact
I am open to academic discussions, guest talks, and joint research collaborations.
Affiliation & Office
Visual Computing Lab
Department of Computational and Data Sciences (CDS)
Indian Institute of Science (IISc)
CV Raman Rd, Bengaluru, Karnataka 560012, India
Email Correspondence
Primary: siladittyam@iisc.ac.in
Alternate: siladittya.manna@gmail.com