This paper proposes a novel Largest Distance Client Selection (LDCS) method that prioritizes clients based on the divergence of their local models from the global model, as quantified by the Frobenius norm, thereby improving training efficiency and model performance while overcoming the limitations of existing random or loss-based approaches.
Federated learning (FL) is an emerging distributed machine learning framework that enables collaborative learning among multiple parties while preserving data privacy. However, the complexity of environments and node heterogeneity in the real world result in uneven data distribution across nodes, leading to Non-IID (No...
Furui Zhan, Ziyu Deng, Yingxin Liu et al.· IEEE Transactions on Informa...· 0 citations
Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. Thi...
Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a persona...
Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay· 0 citations
A review of federated learning through a structured taxonomy that covers its core architectural paradigms, major learning types, model training approaches, and aggregation mechanisms, and analyzes the principal challenges confronting FL, including privacy and security risks, statistical and system heterogeneity, commun...
Mahdiyeh Velaei, Hosna Ghahramani, Ali Ghaffari et al.· Cluster Computing· 0 citations
The fast growth of big data, along with the growing stricter privacy laws, poses tremendous challenges to the traditional centralized implementation of AI models. Federated Learning (FL) offers a decentralized solution; however, it is undermined by a number of performance limitations such as large communicational overh...
Shashi Purna Kurapathi· International Conference Com...· 0 citations
A novel FL framework is presented, FedPhoenix, that stochastically re-sets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific overfitting features.
Jia-Hao Wu, Ming Hu, Yanxin Yang et al.· Neural Information Processin...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
