Non-independent and identically distributed (Non-IID) data across edge
clients have long posed significant challenges to federated learning (FL)
training in edge computing environments. Prior works have proposed various
methods to mitigate this statistical heterogeneity. While these works can
achieve good theoretical performance, in this work we provide the first
investigation into a hidden over-correction phenomenon brought by the uniform
model correction coefficients across clients adopted by existing methods. Such
over-correction could degrade model performance and even cause failures in
model convergence. Um dies anzugehen, we propose TACO, a novel algorithm that
addresses the non-IID nature of clients’ data by implementing fine-grained,
client-specific gradient correction and model aggregation, steering local
models towards a more accurate global optimum. Moreover, we verify that leading
FL algorithms generally have better model accuracy in terms of communication
rounds rather than wall-clock time, resulting from their extra computation
overhead imposed on clients. To enhance the training efficiency, TACO deploys a
lightweight model correction and tailored aggregation approach that requires
minimum computation overhead and no extra information beyond the synchronized
model parameters. To validate TACO’s effectiveness, we present the first FL
convergence analysis that reveals the root cause of over-correction. Extensive
experiments across various datasets confirm TACO’s superior and stable
performance in practice.

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