@inproceedings {pub5735pub5935,
	title = {Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration
},
	author = {Christian Intern{\'o} AND Elena Raponi AND Niki van Stein AND Thomas B{\"a}ck AND Markus Olhofer AND Yaochu Jin AND Barbara Hammer},
	year = {2024},
	month = {December},
	abstract = {The rapid proliferation of smart devices coupled with the advent of 6G networks
has profoundly reshaped the domain of collaborative machine learning. Alongside
growing privacy-security concerns in sensitive fields, these developments have
positioned federated learning (FL) as a pivotal technology for decentralized model
training. Despite its vast potential, specially in the age of complex foundation
models, FL encounters challenges such as elevated communication costs, computational constraints, and the complexities of non-IID data distributions. We introduce
AutoFLIP, an innovative approach that utilizes a federated loss exploration phase
to drive adaptive hybrid pruning, operating in a structured and unstructured way.
This innovative mechanism automatically identifies and prunes model substructure by distilling knowledge on model gradients behavior across different non-IID
client losses topology, thereby optimizing computational efficiency and enhancing
model performance on resource-constrained scenarios. Extensive experiments on
various datasets and FL tasks reveal that AutoFLIP not only efficiently accelerates
global convergence, but also achieves superior accuracy and robustness compared
to traditional methods. On average, AutoFLIP reduces computational overhead by
48.8\% and communication costs by 35.5\%, while improving global accuracy. By
significantly reducing these overheads, AutoFLIP offer the way for efficient FL
deployment in real-world applications for a scalable and broad applicability},
	publisher = {Conference on Neural Information Processing Systems (NeurIPS)},
	booktitle = {NeurIPS2024, International Workshop on Federated Foundation Models in Conjunction}
}