Federated　learning　(FL)　has　emerged　as　a　promising　paradigm　for　privacy-preserving　collaborative　learning,　enabling　multiple　devices　to　jointly　train　a　global　model　without　sharing　their　raw　data.　However,　the　bias　in　FL　training　significantly　reduces　its　performance.　This　poster　presents　a　novel　FL　algorithm　to　counteract　bias　for　performance　improvement.　First,　we　provide　a　global　perspective　for　analyzing　the　causes　of　bias,　data　heterogeneity　and　transmission　probability.　Then,　we　propose　a　method　that　introduces　a　regularized　local　training　method　and　a　reweighted　aggregation　strategy　to　jointly　mitigate　bias.　Through　extensive　experiments　on　real-world　datasets,　we　demonstrate　that　our　method　outperforms　various　baseline　FL　methods　in　terms　of　convergence　speed　and　accuracy.　©　2024　IEEE.