Federated Edge Intelligence and Edge Caching Mechanisms
نویسندگان
چکیده
Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine contexts, particularly edge intelligence caching applications. Recognizing the prevalent challenges of imbalanced noisy impacting scalability resilience, our study introduces two innovative algorithms crafted FL within peer-to-peer framework. These aim to enhance performance, especially decentralized resource-limited settings. Furthermore, we propose client-balancing Dirichlet sampling algorithm with probabilistic guarantees mitigate oversampling issues, optimizing distribution among clients achieve more accurate reliable model training. Within specifics study, employed 10, 20, 40 Raspberry Pi devices practical scenario, simulating real-world conditions. The well-known FedAvg was implemented, enabling multi-epoch client training before weight integration. Additionally, examined influence dataset noise, culminating performance analysis that underscores how novel methods research significantly advance robust efficient techniques, thereby enhancing overall effectiveness applications, including caching.
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ژورنال
عنوان ژورنال: Information
سال: 2023
ISSN: ['2078-2489']
DOI: https://doi.org/10.3390/info14070414