To evaluate the effectiveness of Sprinkles alongside infant and young child feeding (IYCF) education compared with IYCF education alone on anemia, deficiencies in iron, vitamin A, and zinc, and growth in Cambodian infants.Design: Cluster-randomized effectiveness study.Setting: Cambodian rural health district.Participants: Among 3112 infants aged 6 months, a random subsample (n = 1350) was surveyed at baseline and 6-month intervals to age 24 months.Intervention: Daily micronutrient Sprinkles alongside IYCF education vs IYCF education alone for 6 months from ages 6 to 11 months.Main Outcome Measures: Prevalence of anemia; iron, vitamin A, and zinc deficiencies; and growth via biomarkers and anthropometry.Results: Anemia prevalence (hemoglobin level Ͻ11.0 g/dL [to convert to grams per liter, multiply by 10.0]) was reduced in the intervention arm compared with the control arm by 20.6% at 12 months (95% CI, 9.4-30.2; P = .001), and the prevalence of moderate anemia (hemoglobin level Ͻ10.0 g/dL) was reduced by 27.1% (95% CI, 21.0-31.8; PϽ .001). At 12 and 18 months, iron deficiency prevalence was reduced by 23.5% (95% CI, 15.6-29.1; PϽ.001) and 11.6% (95% CI, 2.6-17.9; P=.02), respectively. The mean serum zinc concentration was increased at 12 months (2.88 µg/dL [to convert to micromoles per liter, multiply by 0.153]; 95% CI, 0.26-5.42; P =.03). There was no statistically significant difference in the prevalence of zinc and vitamin A deficiencies or in growth at any time.Conclusions: Sprinkles reduced anemia and iron deficiency and increased the mean serum zinc concentration in Cambodian infants. Anemia and zinc effects did not persist beyond the intervention period.

To build intelligent model learning in conventional architecture, the local data are required to be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage of personalization, and insufficient use of network resources. To address these issues, federated learning (FL) is introduced by offering a systematical framework that converges the distributed modeling process between local participants and the parameter server. However, the challenging issues of insufficient participant scheduling, aggregation policies, model offloading, and resource management still remain within conventional FL architecture. In this survey article, the state-of-the-art solutions for optimizing the orchestration in FL communications are presented, primarily querying the deep reinforcement learning (DRL)-based autonomy approaches. The correlations between the DRL and FL mechanisms are described within the optimized system architectures of selected literature approaches. The observable states, configurable actions, and target rewards are inquired into to illustrate the applicability of DRL-assisted control toward self-organizing FL systems. Various deployment strategies for Internet of Things applications are discussed. Furthermore, this article offers a review of the challenges and future research perspectives for advancing practical performances. Advanced solutions in these aspects will drive the applicability of converged DRL and FL for future autonomous communication-efficient and privacy-aware learning.

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