Handling non-iid data in federated learning: An experimental evaluation towards unified metrics
M Haller, C Lenz, R Nachtigall… - 2023 IEEE Intl Conf …, 2023 - ieeexplore.ieee.org
Recent research has demonstrated that Non-Identically Distributed (Non-IID) data can
negatively impact the performance of global models constructed in federated learning. To …
negatively impact the performance of global models constructed in federated learning. To …
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning
Large Language Models (LLMs) have become instrumental in advancing software
engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. Like …
engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. Like …
Toward efficient resource utilization at edge nodes in federated learning
Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a
global model without sharing their data. This is accomplished by devices computing local …
global model without sharing their data. This is accomplished by devices computing local …
MPCFL: Towards Multi-party Computation for Secure Federated Learning Aggregation
H Kaminaga, FM Awaysheh, S Alawadi… - Proceedings of the IEEE …, 2023 - dl.acm.org
In the rapidly evolving machine learning (ML) and distributed systems realm, the escalating
concern for data privacy naturally comes to the forefront of discussions. Federated learning …
concern for data privacy naturally comes to the forefront of discussions. Federated learning …