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While reusing stale model updates from inactive clients is a common technique to reduce this variance, we find that with skewed client participation, the resulting update staleness can become severe enough to destabilize training. To remedy this, we propose FedSteer, a novel method that constructs a gradient subspace from a cache of recent client gradients to serve as a low-dimensional representation of the current optimization landscape. FedSteer projects an active client's true gradient onto this subspace to find a set of optimal coordinates. For an inactive client, FedSteer reuses these coordinates with the now-evolved subspace drifted by other active clients. This process effectively \"steers\" outdated gradients toward the current global objective. 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For an inactive client, FedSteer reuses these coordinates with the now-evolved subspace drifted by other active clients. This process effectively \"steers\" outdated gradients toward the current global objective. 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