Advances in Federated and Collaborative Learning with Applications for Generative AI
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 15 October 2025 | Viewed by 61
Special Issue Editors
Interests: networks; large-scale optimization; opinion dynamics and graph learning; generative artificial intelligence (AI); AI for health-tech
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid adoption of Generative AI has introduced both exciting opportunities and critical challenges across machine learning, artificial intelligence, and software engineering. A fundamental aspect of advancing these fields is understanding entropy and perplexity, which serve as key measures of uncertainty, information complexity, and model efficiency. These information-theoretic insights are essential for developing scalable and robust AI systems, particularly in handling large-scale, distributed, and privacy-sensitive data.
Federated and collaborative learning play a central role in enabling decentralized AI training, allowing for models to be trained across multiple organizations without centralizing data. However, these frameworks introduce challenges such as model synchronization, communication overhead, and data heterogeneity. Entropy-based optimization techniques, including redundancy reduction and perplexity-aware learning, can enhance efficiency by minimizing unnecessary information transfer while preserving model performance.
In-context learning, an emerging capability in Generative AI, enables models to adapt to new tasks with minimal fine-tuning by leveraging contextual information within prompts. From an information-theoretic perspective, prompt compression and query optimization can be framed as rate distortion and entropy optimization techniques, reducing perplexity and enhancing retrieval efficiency. Optimizing prompt structures not only improves computational efficiency, but also reduces the number of tokens required for effective reasoning in real-world applications.
To further mitigate computational bottlenecks, techniques such as low-rank approximations, structured sparsity, and quantization leverage entropy constraints to improve model compression and inference speed in transformer architectures. These methods enable the scalability of generative AI while maintaining model accuracy, ensuring efficient deployment in edge computing and distributed environments. Additionally, perplexity-aware adaptation mechanisms allow for AI systems to dynamically adjust their complexity based on input uncertainty, improving robustness and generalization. Moreover, the theory of unsupervised learning with large language models shares fundamental connections with Kolmogorov complexity and Shannon’s information theory, highlighting the role of compression and entropy in efficient representation learning.
Another emerging research direction is machine unlearning, where specific data points are selectively removed from trained models without requiring full retraining. From an information-theoretic perspective, this can be viewed as a process of reducing redundant or biased information while preserving model integrity. Techniques such as gradient-based data removal algorithms, model pruning have significant implications for privacy, security, and regulatory compliance.
This Special Issue invites submissions that explore entropy, perplexity, and information-theoretic approaches to enhancing the computational efficiency of Generative AI, particularly in federated and collaborative learning. Topics of interest include, but are not limited to, the following:
- Entropy and perplexity analysis of large language models, including Kolmogorov complexity bounds and information-theoretic optimization;
- Federated and collaborative learning with entropy-aware compression and privacy-preserving techniques;
- Representation learning in decentralized environment;
- Parallel and distributed computing strategies for large-scale AI models;
- Perplexity-constrained compression and query optimization in in-context learning;
- Prompt compression and entropy-minimized retrieval techniques;
- Data removal methods for machine unlearning, focusing on entropic efficiency;
- Scalable techniques to detect AI-generated data using statistical and information-theoretic tools;
- Applications in AI-assisted software, autonomous systems, and edge computing.
This Special Issue aims to bridge the gap between entropy-based learning, perplexity-aware optimization, and AI efficiency, fostering new advancements in generative AI research.
Dr. Chee Wei Tan
Prof. Dr. Wenyi Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- entropy and perplexity
- model interpretation and representation learning
- federated learning and collaborative learning
- machine unlearning
- in-context learning
- distributed optimization
- large language models
- AI safety
- data removal algorithms
- AI-generated data detection
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