Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

Abstract

We advocate a new resource allocation framework, which we term resource rationing, for wireless federated learning (FL). Unlike existing resource allocation methods for FL, resource rationing focuses on balancing resources across learning rounds so that their collective impact on the federated learning performance is explicitly captured. This new framework can be integrated seamlessly with existing resource allocation schemes to optimize the convergence of FL. In particular, a novel "later-isbetter" principle is at the front and center of resource rationing, which is validated empirically in several instances of wireless FL. We also point out technical challenges and research opportunities that are worth pursuing. Resource rationing highlights the benefits of treating the emerging FL as a new class of service that has its own characteristics, and designing communication algorithms for this particular service.

Effect of Combination of Mangrove (Rhizophora apiculata) Leaves and Mirasolia diversifolia in Rations on Production Performance of Goats

“…Similarly, Solon-Biet et al ( 2014) mentioned that the amount of ration consumption is strongly influenced by the levels of energy and protein in the ration. Rations with similar energy content result in ration consumption values that do not significantly differ (Shen et al 2021). Mangrove leaf hay still contains a fairly high crude fiber content of 14.87% (Table 1), so even though the percentage of its use is reduced in the ration to T4, it still has no significant effect on crude fiber consumption.…”