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https://cis.temple.edu/~jiewu/research/publications/Publication_files/Privacy-Preserving_Federated_Neural_Architecture_Search_With_Enhanced_Robustness_for_Edge_Computing.pdf
It enables a group of users to collaboratively train a shared global model [3] or multiple personalized models [12], [13], while keeping their local data private. Supposed that there are clients K = {e1, e2, . . . , e , and } each client k possesses a dataset k Nk {(xj, yj)} . In hori- e D := j=1
https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICDE2024_Online_Federated_Learning_on_Distributed_Unknown_Data_Using_UAVs.pdf
For the energy consumption during the learning phase, we set e1 = 0.01J and e2 = 80J [18]. To better align with real-world data collection scenarios, we design fine-grained PoI data models from three perspectives: data distribution, data generation patterns, and data quality.