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However, real-world deployments are severely hindered by high uplink communication overhead and heterogeneous on-device computational constraints. To address these joint bottlenecks, We present Energy-Aware Adaptive Quantization and Freezing (EA-AQF), a unified framework that co-optimizes communication and computation. EA-AQF introduces a tiered mapping policy that assigns stage-wise activation bitwidths based on a dynamic client resource indicator. To further reduce local training costs, we implement a deterministic, precision-conditioned stage-freezing policy that disables gradient updates for computationally expensive network stages during backpropagation. Additionally, weight quantization is applied to client updates to minimize the communication footprint. We evaluate EA-AQF across diverse architectures, including CNN-7, CNN-17, and ResNet-20 on CIFAR-10, CIFAR-100, and MNIST datasets, and LSTM and MLP on IMDB and HAR datasets, under varying statistical distributions. Experimental results demonstrate that EA-AQF achieves a consistent communication reduction of 72.9% to 74.8% and significant computation reduction up to 12.66\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , which is 1.6\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    higher than prior state-of-the-art methods, with a marginal accuracy trade-off typically within 2-3% of full-precision baselines. Quantitatively, our sustainability analysis reveals that EA-AQF achieves up to 65% reduction in energy consumption compared to state-of-the-art, while strictly limiting carbon emissions to as low as 0.0122\u00a0\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\mathrm {kgCO_2e}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    for resource-intensive tasks. Notably, in highly heterogeneous tasks, EA-AQF maintains robust convergence positioning it as a highly efficient solution for sustainable edge AI.\n                  <\/jats:p>","DOI":"10.1007\/s10586-026-06503-0","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T12:06:26Z","timestamp":1787659586000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EA-AQF: energy aware adaptive quantization and freezing in federated learning"],"prefix":"10.1007","volume":"29","author":[{"given":"Farwa","family":"Ikram","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6309-2892","authenticated-orcid":false,"given":"Sadi","family":"Alawadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dipanwita","family":"Thakur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Guzzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giancarlo","family":"Fortino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"6503_CR1","first-page":"1273","volume":"3","author":"B McMahan","year":"2017","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. 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