Federated learning (FL) provides a collaborative framework that enables intelligent networking devices to train a shared model without the need to share local data. FL has been applied in communication networks, which offers the dual advantage of preserving user privacy and reducing communication overhead. Networking systems and FL are highly complementary. Networking environments provide critical support for data acquisition, edge computing capabilities, round communication/connectivity, and scalable topologies. In turn, FL can leverage capabilities to achieve learning adaptation, low-latency operation, edge intelligence, personalization, and, notably, privacy preservation. In our review, we gather relevant literature and open-source platforms that point out the feasibility of conducting experiments at the confluence of FL and intelligent networking. Our review is structured around key sections, including the introduction of FL concepts, the background of FL applied in networking, and experimental simulations covering networking for FL and FL for networking. Additionally, we delved into case studies showcasing FL potential in optimizing state-of-the-art network optimization objectives, such as learning performance, quality of service, energy, and cost. We also addressed the challenges and outlined future research directions that provide valuable guidance to researchers and practitioners in this trending field.
Citation: Seyha Ros, Prohim Tam, Inseok Song, Seungwoo Kang, Seokhoon Kim. A survey on state-of-the-art experimental simulations for privacy-preserving federated learning in intelligent networking[J]. Electronic Research Archive, 2024, 32(2): 1333-1364. doi: 10.3934/era.2024062
Federated learning (FL) provides a collaborative framework that enables intelligent networking devices to train a shared model without the need to share local data. FL has been applied in communication networks, which offers the dual advantage of preserving user privacy and reducing communication overhead. Networking systems and FL are highly complementary. Networking environments provide critical support for data acquisition, edge computing capabilities, round communication/connectivity, and scalable topologies. In turn, FL can leverage capabilities to achieve learning adaptation, low-latency operation, edge intelligence, personalization, and, notably, privacy preservation. In our review, we gather relevant literature and open-source platforms that point out the feasibility of conducting experiments at the confluence of FL and intelligent networking. Our review is structured around key sections, including the introduction of FL concepts, the background of FL applied in networking, and experimental simulations covering networking for FL and FL for networking. Additionally, we delved into case studies showcasing FL potential in optimizing state-of-the-art network optimization objectives, such as learning performance, quality of service, energy, and cost. We also addressed the challenges and outlined future research directions that provide valuable guidance to researchers and practitioners in this trending field.
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Figures(6) / Tables(9)
Seyha Ros, Prohim Tam, Inseok Song, Seungwoo Kang, Seokhoon Kim. A survey on state-of-the-art experimental simulations for privacy-preserving federated learning in intelligent networking[J]. Electronic Research Archive, 2024, 32(2): 1333-1364. doi: 10.3934/era.2024062
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