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Server momentum is a weighted-history mechanism for aggregating updates, usually implemented as an exponential moving average (EMA): after each round of aggregation, the server does not directly adopt the average gradient of the current round but instead mixes it with the model parameters of the previous round at a certain weight.
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11/2/2026
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UnverifiedIn the FedAvg (Federated Averaging) algorithm, the server distributes the global model to each client, each party completes several rounds of gradient updates locally and then uploads model weights or gradients, and the server generates a new global model by taking a weighted average according to sample size.63% similarUnverifiedA model ID with a date suffix (such as -1017) reflects the model version pinning mechanism; providers continually update the weights on the same model line, causing the output behavior of a model with the same name to drift over time.60% similarUnverifiedJEPA 引入非对称结构，其中目标编码器的梯度通常通过指数移动平均（EMA）更新，而非直接反向传播58% similarUnverified前向传播（forward pass）描述数据从输入层经过每一层网络变换最终输出预测结果的完整流程58% similarUnverifiedDSH将Model与Harness之间的关系定义为动态循环：模型决定下一步做什么，Harness让这一步真实发生，并让结果进入下一步输入58% similar
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